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  • 1.
    Aliakbari, Massume
    et al.
    Department of Crop Production and Plant Breeding, Shiraz University, Shiraz, Iran.
    Cohen, Stephen P.
    Department of Plant Pathology, The Ohio State University, USA.
    Lindlöf, Angelica
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningsmiljön Systembiologi.
    Shamloo-Dashtpagerdi, Roohollah
    Department of Agriculture and Natural Resources, Higher Education Center of Eghlid, Iran.
    Rubisco activase A (RcaA) is a central node in overlapping gene network of drought and salinity in Barley (Hordeum vulgare L.) and may contribute to combined stress tolerance2021Ingår i: Plant physiology and biochemistry (Paris), ISSN 0981-9428, E-ISSN 1873-2690, Vol. 161, s. 248-258Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Co-occurrence of abiotic stresses, especially drought and salinity, is a natural phenomenon in field conditions and is worse for crop production than any single stress. Nowadays, rigorous methods of meta-analysis and systems biology have made it possible to perform cross-study comparisons of single stress experiments, which can uncover main overlapping mechanisms underlying tolerance to combined stress. In this study, a meta-analysis of RNA-Seq data was conducted to obtain the overlapping gene network of drought and salinity stresses in barley (Hordeum vulgare L.), which identified Rubisco activase A (RcaA) as a hub gene in the dual-stress response. Thereafter, a greenhouse experiment was carried out using two barley genotypes with different abiotic stress tolerance and evaluated several physiochemical properties as well as the expression profile and protein activity of RcaA. Finally, machine learning analysis was applied to uncover relationships among combined stress tolerance and evaluated properties. We identified 441 genes which were differentially expressed under both drought and salinity stress. Results revealed that the photosynthesis pathway and, in particular, the RcaA gene are major components of the dual-stress responsive transcriptome. Comparative physiochemical and molecular evaluations further confirmed that enhanced photosynthesis capability, mainly through regulation of RcaA expression and activity as well as accumulation of proline content, have a significant association with combined drought and salinity stress tolerance in barley. Overall, our results clarify the importance of RcaA in combined stress tolerance and may provide new insights for future investigations. 

  • 2.
    Anders, Patrizia
    Högskolan i Skövde, Institutionen för kommunikation och information.
    A bioinformaticians view on the evolution of smell perception2006Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Background:

    The origin of vertebrate sensory systems still contains many mysteries and thus challenges to bioinformatics. Especially the evolution of the sense of smell maintains important puzzles, namely the question whether or not the vomeronasal system is older than the main olfactory system. Here I compare receptor sequences of the two distinct systems in a phylogenetic study, to determine their relationships among several different species of the vertebrates.

    Results:

    Receptors of the two olfactory systems share little sequence similarity and prove to be a challenge in multiple sequence alignment. However, recent dramatical improvements in the area of alignment tools allow for better results and high confidence. Different strategies and tools were employed and compared to derive a

    high quality alignment that holds information about the evolutionary relationships between the different receptor types. The resulting Maximum-Likelihood tree supports the theory that the vomeronasal system is rather an ancestor of the main olfactory system instead of being an evolutionary novelty of tetrapods.

    Conclusions:

    The connections between the two systems of smell perception might be much more fundamental than the common architecture of receptors. A better understanding of these parallels is desirable, not only with respect to our view on evolution, but also in the context of the further exploration of the functionality and complexity of odor perception. Along the way, this work offers a practical protocol through the jungle of programs concerned with sequence data and phylogenetic reconstruction.

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  • 3.
    Andersson, Malin
    Högskolan i Skövde, Institutionen för datavetenskap.
    A method for identification of putatively co-regulated genes2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    The genomes of several organisms have been sequenced and the need for methods to analyse the data is growing. In this project a method is described that tries to identify co-regulated genes. The method identifies transcription factor binding sites, documented in TRANSFAC, in the non-coding regions of genes. The algorithm counts the number of common binding sites and the number of unique binding sites for each pair of genes and decides if the genes are co-regulated. The result of the method is compared with the correlation between the gene expression patterns of the genes. The method is tested on 21 gene pairs from the genome of Saccharomyces cerevisiae. The algorithm first identified binding sites from all organisms. The accuracy of the program was very low in this case. When the algorithm was modified to only identify binding sites found in plants the accuracy was much improved, from 52% to 76% correct predictions.

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  • 4.
    Axelsson, K. F.
    et al.
    Department of Orthopaedic Surgery, Skaraborg Hospital, Skövde, Sweden / Geriatric Medicine, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.
    Wallander, M.
    Geriatric Medicine, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden / Department of Medicine Huddinge, Karolinska Institute, Stockholm, Sweden.
    Johansson, H.
    Institute for Health and Ageing, Catholic University of Australia, Melbourne, Vic., Australia.
    Lundh, Dan
    Högskolan i Skövde, Institutionen för hälsa och lärande. Högskolan i Skövde, Forskningsspecialiseringen Hälsa och Lärande.
    Lorentzon, M.
    Geriatric Medicine, Department of Internal Medicine and ClinicalNutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden / Geriatric Medicine, Sahlgrenska University Hospital, Mölndal, Sweden.
    Hip fracture risk and safety with alendronate treatment in the oldest-old2017Ingår i: Journal of Internal Medicine, ISSN 0954-6820, E-ISSN 1365-2796, Vol. 282, nr 6, s. 546-559Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background. There is high evidence for secondary prevention of fractures, including hip fracture, with alendronate treatment, but alendronate's efficacy to prevent hip fractures in the oldest-old (80 years old), the population with the highest fracture risk, has not been studied. Objective. To investigate whether alendronate treatment amongst the oldest-old with prior fracture was related to decreased hip fracture rate and sustained safety. Methods. Using a national database of men and women undergoing a fall risk assessment at a Swedish healthcare facility, we identified 90 795 patients who were 80 years or older and had a prior fracture. Propensity score matching (four to one) was then used to identify 7844 controls to 1961 alendronate-treated patients. The risk of incident hip fracture was investigated with Cox models and the interaction between age and treatment was investigated using an interaction term. Results. The case and control groups were well balanced in regard to age, sex, anthropometrics and comorbidity. Alendronate treatment was associated with a decreased risk of hip fracture in crude (hazard ratio (HR) 0.62 (0.49-0.79), P < 0.001) and multivariable models (HR 0.66 (0.51-0.86), P < 0.01). Alendronate was related to reduced mortality risk (HR 0.88 (0.82-0.95) but increased risk of mild upper gastrointestinal symptoms (UGI) (HR 1.58 (1.12-2.24). The alendronate association did not change with age for hip fractures or mild UGI. Conclusion. In old patients with prior fracture, alendronate treatment reduces the risk of hip fracture with sustained safety, indicating that this treatment should be considered in these high-risk patients.

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  • 5.
    Birkmeier, Bettina
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Integrating Prior Knowledge into the Fitness Function of an Evolutionary Algorithm for Deriving Gene Regulatory Networks2006Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    The topic of gene regulation is a major research area in the bioinformatics community. In this thesis prior knowledge from Gene Ontology in the form of templates is integrated into the fitness function of an evolutionary algorithm to predict gene regulatory networks. The resulting multi-objective fitness functions are then tested with MAPK network data taken from KEGG to evaluate their respective performances. The results are presented and analyzed. However, a clear tendency cannot be observed. The results are nevertheless promising and can provide motivation for further research in that direction. Therefore different ideas and approaches are suggested for future work.

  • 6.
    Bååth, Rasmus
    et al.
    Lund University Cognitive Science, Lund University, LUX, Lund, Sweden.
    Lagerstedt, Erik
    Lund University Cognitive Science, Lund University, LUX, Lund, Sweden.
    Gärdenfors, Peter
    Lund University Cognitive Science, Lund University, LUX, Lund, Sweden.
    A prototype-based resonance model of rhythm categorization2014Ingår i: i-Perception, E-ISSN 2041-6695, Vol. 5, nr 6, s. 548-558Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Categorization of rhythmic patterns is prevalent in musical practice, an example of this being the transcription of (possibly not strictly metrical) music into musical notation. In this article we implement a dynamical systems' model of rhythm categorization based on the resonance theory of rhythm perception developed by Large (2010). This model is used to simulate the categorical choices of participants in two experiments of Desain and Honing (2003). The model accurately replicates the experimental data. Our results support resonance theory as a viable model of rhythm perception and show that by viewing rhythm perception as a dynamical system it is possible to model central properties of rhythm categorization.

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  • 7.
    Bååth, Rasmus
    et al.
    Lund University Cognitive Science.
    Lagerstedt, Erik
    Lund University Cognitive Science.
    Gärdenfors, Peter
    Lund University Cognitive Science.
    An Oscillator Model of Categorical Rhythm Perception2013Ingår i: Cooperative Minds: Social Interaction and Group Dynamics: Proceedings of the 35th Annual Meeting of the Cognitive Science Society, Berlin, Germany, July 31-August 3, 2013 / [ed] Markus Knauff, Natalie Sebanz, Michael Pauen, Ipke Wachsmuth, Austin, TX: Cognitive Science Society, Inc., 2013, s. 1803-1808Konferensbidrag (Refereegranskat)
    Abstract [en]

    Categorical perception is a well studied phenomenon in, for example, colour perception, phonetics and music. In this article we implement a dynamical systems model of categorical rhythm perception based on the resonance theory of rhythm perception developed by Large (2010). This model is used to simulate the categorical choices of participants in two experiments of Desain and Honing (2003). The model is able to accurately replicate the experimental data. Our results supports that resonance theory is a viable model of rhythm perception and they show that by viewing rhythm perception as a dynamical system it is possible to model properties of categorical perception.

  • 8.
    Chaliha, Jaysmita Khanindra
    Högskolan i Skövde, Institutionen för biovetenskap.
    Mathematical modelling simulation data and artificial intelligence for the study of tumour-macrophage interaction2023Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    The study explores the integration of mathematical modelling and machine learning to understand tumour-macrophage interactions in the tumour microenvironment. It details mathematical models based on biochemistry and physics for predicting tumour dynamics, highlighting the role of macrophages. Machine learning, particularly unsupervised and supervised techniques like K-means clustering, logistic regression, and support vector machines, are implemented to analyse simulation data. The thesis's integration of K-means clustering reveals distinct tumour behaviour patterns through the classification of tumour cells based on their microenvironmental interactions. This segmentation is crucial for understanding tumour heterogeneity and its implications for treatment. Additionally, the application of logistic regression provides insights into the probability of macrophage polarization states in the tumour microenvironment. This statistical model underscores the significant factors influencing macrophage behaviour and their consequent impact on tumour progression. These analytical approaches enhance the understanding of the complex dynamics within the tumour microenvironment, contributing to more effective tumour study strategies. The study presents a comprehensive analysis of tumour growth, macrophage polarization, and their impact on cancer treatment and prognosis. Ethical considerations and future directions focus on enhancing model accuracy and integrating experimental data for improved cancer diagnosis and treatment strategies. The thesis concludes with the potential of this hybrid approach in advancing cancer biology and therapeutic approaches.

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  • 9.
    Chawade, Aakash
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Inferring Gene Regulatory Networks in Cold-Acclimated Plants by Combinatorial Analysis of mRNA Expression Levels and Promoter Regions2006Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats/Examensarbete
    Abstract [en]

    Understanding the cold acclimation process in plants may help us develop genetically engineered plants that are resistant to cold. The key factor in understanding this process is to study the genes and thus the gene regulatory network that is involved in the cold acclimation process. Most of the existing approaches1-8 in deriving regulatory networks rely only on the gene expression data. Since the expression data is usually noisy and sparse the networks generated by these approaches are usually incoherent and incomplete. Hence a new approach is proposed here that analyzes the promoter regions along with the expression data in inferring the regulatory networks. In this approach genes are grouped into sets if they contain similar over-represented motifs or motif pairs in their promoter regions and if their expression pattern follows the expression pattern of the regulating gene. The network thus derived is evaluated using known literature evidence, functional annotations and from statistical tests.

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  • 10.
    Chen, Lei
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Construction of Evolutionary Tree Models for Oncogenesis of Endometrial Adenocarcinoma2005Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats/Examensarbete
    Abstract [en]

    Endometrial adenocarcinoma (EAC) is the fourth leading cause of carcinoma in woman worldwide, but not much is known about genetic factors involved in this complex disease. During the EAC process, it is well known that losses and gains of chromosomal regions do not occur completely at random, but partly through some flow of causality. In this work, we used three different algorithms based on frequency of genomic alterations to construct 27 tree models of oncogenesis. So far, no study about applying pathway models to microsatellite marker data had been reported. Data from genome–wide scans with microsatellite markers were classified into 9 data sets, according to two biological approaches (solid tumor cell and corresponding tissue culture) and three different genetic backgrounds provided by intercrossing the susceptible rat BDII strain and two normal rat strains. Compared to previous study, similar conclusions were drawn from tree models that three main important regions (I, II and III) and two subordinate regions (IV and V) are likely to be involved in EAC development. Further information about these regions such as their likely order and relationships was produced by the tree models. A high consistency in tree models and the relationship among p19, Tp53 and Tp53 inducible

    protein genes provided supportive evidence for the reliability of results.

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  • 11.
    Crone, Sven F.
    et al.
    Lancaster University Management School, Department of Management Science, Centre for Forecasting, Bailrigg campus, Lancaster, United Kingdom.
    Kourentzes, Nikolaos
    Lancaster University Management School, Department of Management Science, Centre for Forecasting, Bailrigg campus, Lancaster, United Kingdom.
    Feature selection for time series prediction: A combined filter and wrapper approach for neural networks2010Ingår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 73, nr 10-12, s. 1923-1936Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Modelling artificial neural networks for accurate time series prediction poses multiple challenges, in particular specifying the network architecture in accordance with the underlying structure of the time series. The data generating processes may exhibit a variety of stochastic or deterministic time series patterns of single or multiple seasonality, trends and cycles, overlaid with pulses, level shifts and structural breaks, all depending on the discrete time frequency in which it is observed. For heterogeneous datasets of time series, such as the 2008 ESTSP competition, a universal methodology is required for automatic network specification across varying data patterns and time frequencies. We propose a fully data driven forecasting methodology that combines filter and wrapper approaches for feature selection, including automatic feature evaluation, construction and transformation. The methodology identifies time series patterns, creates and transforms explanatory variables and specifies multilayer perceptrons for heterogeneous sets of time series without expert intervention. Examples of the valid and reliable performance in comparison to established benchmark methods are shown for a set of synthetic time series and for the ESTSP'08 competition dataset, where the proposed methodology obtained second place. 

  • 12.
    Dodda, Srinivasa Rao
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Improvements and extensions of a web-tool for finding candidate genes associated with rheumatoid arthritis2005Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats/Examensarbete
    Abstract [en]

    QuantitativeTraitLocus (QTL) is a statistical method used to restrict genomic regions contributing to specific phenotypes. To further localize genes in such regions a web tool called “Candidate Gene Capture” (CGC) was developed by Andersson et al. (2005). The CGC tool was based on the textual description of genes defined in the human phenotype database OMIM. Even though the CGC tool works well, the tool was limited by a number of inconsistencies in the underlying database structure, static web pages and some gene descriptions without properly defined function in the OMIM database. Hence, in this work the CGC tool was improved by redesigning its database structure, adding dynamic web pages and improving the prediction of unknown gene function by using exon analysis. The changes in database structure diminished the number of tables considerably, eliminated redundancies and made data retrieval more efficient. A new method for prediction of gene function was proposed, based on the assumption that similarity between exon sequences is associated with biochemical function. Using Blast with 20380 exon protein sequences and a threshold E-value of 0.01, 639 exon groups were obtained with an average of 11 exons per group. When estimating the functional similarity, it was found that on the average 72% of the exons in a group had at least one Gene Ontology (GO) term in common.

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  • 13.
    Engerberg, Malin
    Högskolan i Skövde, Institutionen för datavetenskap.
    Development of database support for production of doubled haploids2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats (Examensarbete)
    Abstract [en]

    In this project relational and Lotus Notes database technology are evaluated with regard to their suitability in providing computer-based support in plant breeding in general and specifically in the production of doubled haploids. The two developed databases are compared based on a set of requirements produced together with the DH-group which is the main users of the databases. The results indicate that both Lotus Notes and the relational databases are able to fulfil all needs documented in this project, although both systems have their limitations. An often expressed opinion is that it is difficult to combine biology and databases. The experience gained in this project however suggests that it does not need to be the case in instances where data is not as complicated as often discussed. Observations made during this project indicate that data warehousing with integrated data mining and OLAP tools are surprisingly similar to how the DH-group at Svalöf Weibull works and could be a suitable solution for the production of doubled haploids.

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  • 14.
    Frykholm, K.
    et al.
    Department of Biology and Biological Engineering, Chalmers University of Technology.
    Nyberg, L. K.
    Department of Biology and Biological Engineering, Chalmers University of Technology.
    Lagerstedt, Erik
    Department of Astronomy and Theoretical Physics, Lund University.
    Noble, C.
    Department of Astronomy and Theoretical Physics, Lund University.
    Fritzsche, J.
    Department of Applied Physics, Chalmers University of Technology.
    Karami, N.
    Department of Clinical Microbiology, Sahlgrenska University Hospital and Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy of the University of Gothenburg.
    Ambjörnsson, T.
    Department of Astronomy and Theoretical Physics, Lund University.
    Sandegren, L.
    Department of Medical Biochemistry and Microbiology, Uppsala University.
    Westerlund, F.
    Department of Biology and Biological Engineering, Chalmers University of Technology.
    Fast size-determination of intact bacterial plasmids using nanofluidic channels2015Ingår i: Lab on a Chip, ISSN 1473-0197, E-ISSN 1473-0189, Vol. 15, nr 13, s. 2739-2743Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We demonstrate how nanofluidic channels can be used as a tool to rapidly determine the number and sizes of plasmids in bacterial isolates. Each step can be automated at low cost, opening up opportunities for general use in microbiology labs.

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  • 15.
    Genheden, Samuel
    Högskolan i Skövde, Institutionen för kommunikation och information.
    A fast protein-ligand docking method2006Självständigt arbete på grundnivå (kandidatexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    In this dissertation a novel approach to protein-ligand docking is presented. First an existing method to predict putative active sites is employed. These predictions are then used to cut down the search space of an algorithm that uses the fast Fourier transform to calculate the geometrical and electrostatic complementarity between a protein and a small organic ligand. A simplified hydrophobicity score is also calculated for each active site. The docking method could be applied either to dock ligands in a known active site or to rank several putative active sites according to their biological feasibility. The method was evaluated on a set of 310 protein-ligand complexes. The results show that with respect to docking the method with its initial parameter settings is too coarse grained. The results also show that with respect to ranking of putative active sites the method works quite well.

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  • 16.
    Gunnarsson, Ida
    Högskolan i Skövde, Institutionen för datavetenskap.
    Deriving Protein Networks by Combining Gene Expression and Protein Chip Analysis2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    In order to derive reliable protein networks it has recently been suggested that the combination of information from both gene and protein level is required. In this thesis a combination of gene expression and protein chip analysis was performed when constructing protein networks. Proteins with high affinity to the same substrates and encoded by genes with high correlation is here thought to constitute reliable protein networks. The protein networks derived are unfortunately not as reliable as were hoped for. According to the tests performed, the method derived in this thesis does not perform more than slightly better than chance. However, the poor results can depend on the data used, since mismatching and shortage of data has been evident.

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  • 17.
    Gustafsson, Sara
    Högskolan i Skövde, Institutionen för datavetenskap.
    Evaluation of analysis methods for identification of differentially expressed genes in oligonucleotide microarray data2003Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    Microarrays are part of a new class of biotechnologies which allow the monitoring of expression levels four thousands of genes simultaneously. The problem is now to make sense of the resulting massive data set. In this thesis the results from five different methods for differential analysis of oligonucleotide microarray data are evaluated. The methods are simple classic t-test and Mann-Whitney U test, the software GeneSpring and Significance Analysis of Microarrays (SAM) and the use of Affymetrix software in combination with a scoring system. The methods are used to analyse two different microarray data sets with different number of replicates. These data sets are further divided in different ways to examine different questions that still are unsolved problems in the microarray technology. The aim of the evaluation is to examine the reliability of the results obtained from differential analysis of microarray data.

  • 18.
    Hettne, Kristina
    Högskolan i Skövde, Institutionen för datavetenskap.
    Using nuclear receptor interactions as biomarkers for metabolic syndrome2003Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    Metabolic syndrome is taking epidemic proportions, especially in developed countries. Each risk factor component of the syndrome independently increases the risk of developing coronary artery disease. The risk factors are obesity, dyslipidemia, hypertension, diabetes type 2, insulin resistance, and microalbuminuria. Nuclear receptors is a family of receptors that has recently received a lot of attention due to their possible involvement in metabolic syndrome. Putting the receptors into context with their co-factors and ligands may reveal therapeutic targets not found by studying the receptors alone. Therefore, in this thesis, interactions between genes in nuclear receptor pathways were analysed with the goal of investigating if these interactions can supply leads to biomarkers for metabolic syndrome. Metabolic syndrome donor gene expression data from the BioExpressä, database was analysed with the APRIORI algorithm (Agrawal et al. 1993) for generating and mining association rules. No association rules were found to function as biomarkers for metabolic syndrome, but the resulting rules show that the data mining technique successfully found associations between genes in signaling pathways.

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  • 19.
    Hillerton, Thomas
    Högskolan i Skövde, Institutionen för biovetenskap.
    Predicting adverse drug reactions in cancer treatment using a neural network based approach2018Självständigt arbete på avancerad nivå (magisterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
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  • 20.
    Holmgren, Gustav
    et al.
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi. Department of Clinical Chemistry and Transfusion Medicine, Institute of Biomedicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden / Takara Bio Europe AB, Gothenburg, Sweden.
    Sartipy, Peter
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi. AstraZeneca Gothenburg, CVMD GMed, GMD, Mölndal, Sweden.
    Andersson, Christian X.
    Takara Bio Europe AB, Gothenburg, Sweden.
    Lindahl, Anders
    Department of Clinical Chemistry and Transfusion Medicine, Institute of Biomedicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
    Synnergren, Jane
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    Expression profiling of human pluripotent stem cell-derived cardiomyocytes exposed to doxorubicin - integration and visualization of multi omics data2018Ingår i: Toxicological Sciences, ISSN 1096-6080, E-ISSN 1096-0929, Vol. 163, nr 1, s. 182-195Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Anthracyclines, such as doxorubicin, are highly efficient chemotherapeutic agents against a variety of cancers. However, anthracyclines are also among the most cardiotoxic therapeutic drugs presently on the market. Chemotherapeutic-induced cardiomyopathy is one of the leading causes of disease and mortality in cancer survivors. The exact mechanisms responsible for doxorubicin-induced cardiomyopathy are not completely known, but the fact that the cardiotoxicity is dose-dependent and that there is a variation in time-to-onset of toxicity, and gender- and age differences suggests that several mechanisms may be involved.In the present study, we investigated doxorubicin-induced cardiotoxicity in human pluripotent stem cell-derived cardiomyocytes using proteomics. In addition, different sources of omics data (protein, mRNA, and microRNA) from the same experimental setup were further combined and analyzed using newly developed methods to identify differential expression in data of various origin and types. Subsequently, the results were integrated in order to generate a combined visualization of the findings.In our experimental model system, we exposed cardiomyocytes derived from human pluripotent stem cells to doxorubicin for up to two days, followed by a wash-out period of additionally 12 days. Besides an effect on the cell morphology and cardiomyocyte functionality, the data show a strong effect of doxorubicin on all molecular levels investigated. Differential expression patterns that show a linkage between the proteome, transcriptome, and the regulatory microRNA network, were identified. These findings help to increase the understanding of the mechanisms behind anthracycline-induced cardiotoxicity and suggest putative biomarkers for this condition.

  • 21.
    Huque, Enamul
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Shape Analysis and Measurement for the HeLa cell classification of cultured cells in high throughput screening2006Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Feature extraction by digital image analysis and cell classification is an important task for cell culture automation. In High Throughput Screening (HTS) where thousands of data points are generated and processed at once, features will be extracted and cells will be classified to make a decision whether the cell-culture is going on smoothly or not. The culture is restarted if a problem is detected. In this thesis project HeLa cells, which are human epithelial cancer cells, are selected for the experiment. The purpose is to classify two types of HeLa cells in culture: Cells in cleavage that are round floating cells (stressed or dead cells are also round and floating) and another is, normal growing cells that are attached to the substrate. As the number of cells in cleavage will always be smaller than the number of cells which are growing normally and attached to the substrate, the cell-count of attached cells should be higher than the round cells. There are five different HeLa cell images that are used. For each image, every single cell is obtained by image segmentation and isolation. Different mathematical features are found for each cell. The feature set for this experiment is chosen in such a way that features are robust, discriminative and have good generalisation quality for classification. Almost all the features presented in this thesis are rotation, translation and scale invariant so that they are expected to perform well in discriminating objects or cells by any classification algorithm. There are some new features added which are believed to improve the classification result. The feature set is considerably broad rather than in contrast with the restricted sets which have been used in previous work. These features are used based on a common interface so that the library can be extended and integrated into other applications. These features are fed into a machine learning algorithm called Linear Discriminant Analysis (LDA) for classification. Cells are then classified as ‘Cells attached to the substrate’ or Cell Class A and ‘Cells in cleavage’ or Cell Class B. LDA considers features by leaving and adding shape features for increased performance. On average there is higher than ninety five percent accuracy obtained in the classification result which is validated by visual classification.

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  • 22.
    Hurme, Mikko
    et al.
    Department of Psychology, University of Turku, Finland / Centre for Cognitive Neuroscience, University of Turku, Finland / Turku Brain and Mind Centre, University of Turku, Finland.
    Koivisto, Mika
    Department of Psychology, University of Turku, Finland / Centre for Cognitive Neuroscience, University of Turku, Finland / Turku Brain and Mind Centre, University of Turku, Finland.
    Revonsuo, Antti
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi. Department of Psychology, University of Turku, Finland / Centre for Cognitive Neuroscience, University of Turku, Finland / Turku Brain and Mind Centre, University of Turku, Finland.
    Railo, Henry
    Department of Psychology, University of Turku, Finland / Centre for Cognitive Neuroscience, University of Turku, Finland / Turku Brain and Mind Centre, University of Turku, Finland.
    Early processing in primary visual cortex is necessary for conscious and unconscious vision while late processing is necessary only for conscious vision in neurologically healthy humans2017Ingår i: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 150, s. 230-238Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The neural mechanisms underlying conscious and unconscious visual processes remain controversial. Blindsight patients may process visual stimuli unconsciously despite their VI lesion, promoting anatomical models, which suggest that pathways bypassing the VI support unconscious vision. On the other hand, physiological models argue that the major geniculostriate pathway via VI is involved in both unconscious and conscious vision, but in different time windows and in different types of neural activity. According to physiological models, feedforward activity via VI to higher areas mediates unconscious processes whereas feedback loops of recurrent activity from higher areas back to VI support conscious vision. With transcranial magnetic stimulation (TMS) it is possible to study the causal role of a brain region during specific time points in neurologically healthy participants. In the present study, we measured unconscious processing with redundant target effect, a phenomenon where participants respond faster to two stimuli than one even when one of the stimuli is not consciously perceived. We tested the physiological feedforward-feedback model of vision by suppressing conscious vision by interfering selectively either with early or later VI activity with TMS. Our results show that early VI activity (60 ms) is necessary for both unconscious and conscious vision. During later processing stages (90 ms), VI contributes selectively to conscious vision. These findings support the feedforward-feedback-model of consciousness.

  • 23.
    Jacobsson, Annelie
    Högskolan i Skövde, Institutionen för datavetenskap.
    Comparing NR Expression among Metabolic Syndrome Risk Factors2003Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    The metabolic syndrome is a cluster of metabolic risk factors such as diabetes type II, dyslipidemia, hypertension, obesity, microalbuminurea and insulin resistance, which in the recent years has increased greatly in many parts of the world. In this thesis decision trees were applied to the BioExpress database, including both clinical data about donors and gene expression data, to investigate nuclear receptors ability to serve as markers for the metabolic syndrome. Decision trees were created and the classification performance for each individual risk factor were then analysed. The rules generated from the risk factor trees were compared in order to search for similarities and dissimilarities. The comparisons of rules were performed in pairs of risk factors, in groups of three and on all risk factors and they resulted in the discovery of a set of genes where the most interesting were the Peroxisome Proliferator Activated Receptor - Alpha, the Peroxisome Proliferator Activated Receptor - Gamma and the Glucocorticoid Receptor. These genes existed in pathways associated with the metabolic syndrome and in the recent scientific literature.

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  • 24.
    Javeed, Ashir
    et al.
    Aging Research Center, Karolinska Institutet, Stockholm, Sweden ; Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Dallora, Ana Luiza
    Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Sanmartin Berglund, Johan
    Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Ali, Arif
    Department of Computer Science, University of Science and Technology Bannu, Pakistan.
    Anderberg, Peter
    Högskolan i Skövde, Institutionen för hälsovetenskaper. Högskolan i Skövde, Forskningsmiljön hälsa, hållbarhet och digitalisering. Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Ali, Liaqat
    Department of Electrical Engineering, University of Science and Technology Bannu, Pakistan.
    Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks2023Ingår i: Computers, Materials and Continua, ISSN 1546-2218, E-ISSN 1546-2226, Vol. 75, nr 2, s. 2491-2508Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset. 

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  • 25.
    Javeed, Ashir
    et al.
    Aging Research Center, Karolinska Institutet, Stockholm, Sweden;Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Dallora, Ana Luiza
    Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Sanmartin Berglund, Johan
    Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Idrisoglu, Alper
    Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Ali, Liaqat
    Department of Electrical Engineering, University of Science and Technology Bannu, Pakistan.
    Rauf, Hafiz Tayyab
    Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent, UK.
    Anderberg, Peter
    Högskolan i Skövde, Institutionen för hälsovetenskaper. Högskolan i Skövde, Forskningsmiljön hälsa, hållbarhet och digitalisering. Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification2023Ingår i: Biomedicines, E-ISSN 2227-9059, Vol. 11, nr 2, artikel-id 439Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly proposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew’s correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.

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  • 26.
    Javeed, Ashir
    et al.
    Aging Research Center, Karolinska Institutet, Solna, Stockholm, Sweden ; Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Sanmartin Berglund, Johan
    Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Dallora, Ana Luiza
    Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Saleem, Muhammad Asim
    Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Chulalongkorn University, Bangkok, Thailand.
    Anderberg, Peter
    Högskolan i Skövde, Institutionen för hälsovetenskaper. Högskolan i Skövde, Forskningsmiljön hälsa, hållbarhet och digitalisering. Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden.
    Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data2023Ingår i: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 16, nr 1, artikel-id 188Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors. 

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  • 27.
    Jurcevic, Sanja
    et al.
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningsmiljön Systembiologi.
    Keane, Simon
    Högskolan i Skövde, Institutionen för hälsovetenskaper. Högskolan i Skövde, Forskningsmiljön hälsa, hållbarhet och digitalisering.
    Borgmästars, Emmy
    Department of Surgical and Perioperative Sciences/Surgery, Umeå University, Sweden.
    Lubovac-Pilav, Zelmina
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningsmiljön Systembiologi.
    Ejeskär, Katarina
    Högskolan i Skövde, Institutionen för hälsovetenskaper. Högskolan i Skövde, Forskningsmiljön hälsa, hållbarhet och digitalisering. Högskolan i Skövde, Institutionen för biovetenskap.
    Bioinformatics analysis of miRNAs in the neuroblastoma 11q-deleted region reveals a role of miR-548l in both 11q-deleted and MYCN amplified tumour cells2022Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 12, nr 1, artikel-id 19729Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Neuroblastoma is a childhood tumour that is responsible for approximately 15% of all childhood cancer deaths. Neuroblastoma tumours with amplification of the oncogene MYCN are aggressive, however, another aggressive subgroup without MYCN amplification also exists; rather, they have a deleted region at chromosome arm 11q. Twenty-six miRNAs are located within the breakpoint region of chromosome 11q and have been checked for a possible involvement in development of neuroblastoma due to the genomic alteration. Target genes of these miRNAs are involved in pathways associated with cancer, including proliferation, apoptosis and DNA repair. We could show that miR-548l found within the 11q region is downregulated in neuroblastoma cell lines with 11q deletion or MYCN amplification. In addition, we showed that the restoration of miR-548l level in a neuroblastoma cell line led to a decreased proliferation of these cells as well as a decrease in the percentage of cells in the S phase. We also found that miR-548l overexpression suppressed cell viability and promoted apoptosis, while miR-548l knockdown promoted cell viability and inhibited apoptosis in neuroblastoma cells. Our results indicate that 11q-deleted neuroblastoma and MYCN amplified neuroblastoma coalesce by downregulating miR-548l.

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  • 28.
    Karjalainen, Merja
    Högskolan i Skövde, Institutionen för datavetenskap.
    Analysing subsets of gene expression data to find putatively co-regulated genes2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    This project is an investigation of whether analysing subsets of time series gene expression data can give additional information about putatively co-regulated genes, compared to only using the whole time series. The original gene expression data set was partitioned into subsets and similarity was computed for both the whole timed series and subsets. Pearson correlation was used as similarity measure between gene expression profiles. The results indicate that analysing co-expression in subsets of gene expression data derives true-positive connections, with respect to co-regulation, that are not detected by only using the whole time series data. Unfortunately, with the actual data set, chosen similarity measure and partitioning of the data, randomly generated connections have the same amount of true-positives as the ones derived by the applied analysis. However, it is worth to continue further analysis of the subsets of gene expression data, which is based on the multi-factorial nature of gene regulation. E.g. other similarity measures, data sets and ways of partitioning the data set should be tried.

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  • 29.
    Keller, Jens
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Clustering biological data using a hybrid approach: Composition of clusterings from different features2008Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Clustering of data is a well-researched topic in computer sciences. Many approaches have been designed for different tasks. In biology many of these approaches are hierarchical and the result is usually represented in dendrograms, e.g. phylogenetic trees. However, many non-hierarchical clustering algorithms are also well-established in biology. The approach in this thesis is based on such common algorithms. The algorithm which was implemented as part of this thesis uses a non-hierarchical graph clustering algorithm to compute a hierarchical clustering in a top-down fashion. It performs the graph clustering iteratively, with a previously computed cluster as input set. The innovation is that it focuses on another feature of the data in each step and clusters the data according to this feature. Common hierarchical approaches cluster e.g. in biology, a set of genes according to the similarity of their sequences. The clustering then reflects a partitioning of the genes according to their sequence similarity. The approach introduced in this thesis uses many features of the same objects. These features can be various, in biology for instance similarities of the sequences, of gene expression or of motif occurences in the promoter region. As part of this thesis not only the algorithm itself was implemented and evaluated, but a whole software also providing a graphical user interface. The software was implemented as a framework providing the basic functionality with the algorithm as a plug-in extending the framework. The software is meant to be extended in the future, integrating a set of algorithms and analysis tools related to the process of clustering and analysing data not necessarily related to biology.

    The thesis deals with topics in biology, data mining and software engineering and is divided into six chapters. The first chapter gives an introduction to the task and the biological background. It gives an overview of common clustering approaches and explains the differences between them. Chapter two shows the idea behind the new clustering approach and points out differences and similarities between it and common clustering approaches. The third chapter discusses the aspects concerning the software, including the algorithm. It illustrates the architecture and analyses the clustering algorithm. After the implementation the software was evaluated, which is described in the fourth chapter, pointing out observations made due to the use of the new algorithm. Furthermore this chapter discusses differences and similarities to related clustering algorithms and software. The thesis ends with the last two chapters, namely conclusions and suggestions for future work. Readers who are interested in repeating the experiments which were made as part of this thesis can contact the author via e-mail, to get the relevant data for the evaluation, scripts or source code.

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  • 30.
    Kristinsson, Vilhelm Yngvi
    Högskolan i Skövde, Institutionen för kommunikation och information.
    The effect of normalization methods on the identification of differentially expressed genes in microarray data2007Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    In this thesis the effect of normalization methods on the identification of differentially expressed genes is investigated. A zebrafish microarray dataset called Swirl was used in this thesis work. First the Swirl dataset was extracted and visualized to view if the robust spline and print tip loess normalization methods are appropriate to normalize this dataset. The dataset was then normalized with the two normalization methods and the differentially expressed genes were identified with the LimmaGUI program. The results were then evaluated by investigating which genes overlap after applying different normalization methods and which ones are identified uniquely after applying the different methods. The results showed that after the normalization methods were applied the differentially expressed genes that were identified by the LimmaGUI program did differ to some extent but the difference was not considered to be major. Thus the main conclusion is that the choice of normalization method does not have a major effect on the resulting list of differentially expressed genes.

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  • 31.
    Laurio, Kim
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    Svensson, Thomas
    Biovitrum AB, Göteborg, Sweden.
    Jirstrand, Mats
    Fraunhofer-Chalmers Research Center for Industrial Mathematics, Göteborg, Sweden.
    Nilsson, Patric
    Högskolan i Skövde, Institutionen för vård och natur. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    Gamalielsson, Jonas
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    Olsson, Björn
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    Evolutionary search for improved path diagrams2007Ingår i: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: 5th European Conference, EvoBIO 2007, Valencia, Spain, April 11-13, 2007. Proceedings / [ed] Elena Marchiori, Jason H. Moore, Jagath C. Rajapakse, Springer Berlin/Heidelberg, 2007, s. 114-121Konferensbidrag (Refereegranskat)
    Abstract [en]

    A path diagram relates observed, pairwise, variable correlations to a functional structure which describes the hypothesized causal relations between the variables. Here we combine path diagrams, heuristics and evolutionary search into a system which seeks to improve existing gene regulatory models. Our evaluation shows that once a correct model has been identified it receives a lower prediction error compared to incorrect models, indicating the overall feasibility of this approach. However, with smaller samples the observed correlations gradually become more misleading, and the evolutionary search increasingly converges on suboptimal models. Future work will incorporate publicly available sources of experimentally verified biological facts to computationally suggest model modifications which might improve the model’s fitness.

  • 32.
    Lindefelt, Lisa
    Högskolan i Skövde, Institutionen för datavetenskap.
    Predicting gene expression using artificial neural networks2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    Today one of the greatest aims within the area of bioinformatics is to gain a complete understanding of the functionality of genes and the systems behind gene regulation. Regulatory relationships among genes seem to be of a complex nature since transcriptional control is the result of complex networks interpreting a variety of inputs. It is therefore essential to develop analytical tools detecting complex genetic relationships.

    This project examines the possibility of the data mining technique artificial neural network (ANN) detecting regulatory relationships between genes. As an initial step for finding regulatory relationships with the help of ANN the goal of this project is to train an ANN to predict the expression of an individual gene. The genes predicted are the nuclear receptor PPAR-g and the insulin receptor. Predictions of the two target genes respectively were made using different datasets of gene expression data as input for the ANN. The results of the predictions of PPAR-g indicate that it is not possible to predict the expression of PPAR-g under the circumstances for this experiment. The results of the predictions of the insulin receptor indicate that it is not possible to discard using ANN for predicting the gene expression of an individual gene.

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  • 33.
    Lorentzon, Fredrik
    Högskolan i Skövde, Institutionen för datavetenskap.
    Data modelling and implementation of a chemical compounds database2003Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    In this project a relational DBMS has been designed, developed and implemented. The RDBMS handles information on cellular behaviour in response to many different chemical compounds. The RDBMS is accessed with a Common Gateway Interface (CGI) programmed with Perl and the new system replaces the previous system that was used at the biotech company Neuronova. The results indicate that the developed RDBMS system will improve the work, especially for the Lead Discovery department. The data is structured in a stricter way. The complete system offers the benefit of integration with other databases at the company including systems for EST sequences and for target discovery.

    The full text is not available due to confidetial parts in it.

  • 34.
    Lövfors, William
    et al.
    Department of Biomedical Engineering, Linköping University, Sweden ; Department of Mathematics, Linköping University, Sweden ; School of Medical Sciences and Inflammatory Response and Infection Susceptibility Centre (iRiSC), Faculty of Medicine and Health, Örebro University, Sweden.
    Magnusson, Rasmus
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningsmiljön Systembiologi.
    Jönsson, Cecilia
    Department of Biomedical Engineering, Linköping University, Sweden ; Department of Health, Medicine and Caring Sciences, Linköping University, Sweden.
    Gustafsson, Mika
    Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Olofsson, Charlotta S.
    Department of Physiology/Metabolic Physiology, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Sweden.
    Cedersund, Gunnar
    Department of Biomedical Engineering, Linköping University, Sweden ; School of Medical Sciences and Inflammatory Response and Infection Susceptibility Centre (iRiSC), Faculty of Medicine and Health, Örebro University, Sweden ; Center for Medical Image Science and Visualization (CMIV), Linköping University, Sweden.
    Nyman, Elin
    Department of Biomedical Engineering, Linköping University, Sweden.
    A comprehensive mechanistic model of adipocyte signaling with layers of confidence2023Ingår i: npj Systems Biology and Applications, E-ISSN 2056-7189, Vol. 9, nr 1, artikel-id 24Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70–90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes. 

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  • 35.
    Martinez Maestre, Andreu
    Högskolan i Skövde, Institutionen för biovetenskap.
    PVC: Proximity Value Clustering: A new clustering method without human interaction2018Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
  • 36.
    Marzec-Schmidt, Katarzyna
    et al.
    Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Skara, Sweden.
    Ghosheh, Nidal
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningsmiljön Systembiologi. Takara Bio Europe, Gothenburg, Sweden.
    Stahlschmidt, Sören Richard
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningsmiljön Systembiologi.
    Küppers-Munther, Barbara
    Takara Bio Europe, Gothenburg, Sweden.
    Synnergren, Jane
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningsmiljön Systembiologi. Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Sweden.
    Ulfenborg, Benjamin
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningsmiljön Systembiologi.
    Artificial intelligence supports automated characterization of differentiated human pluripotent stem cells2023Ingår i: Stem Cells, ISSN 1066-5099, E-ISSN 1549-4918, Vol. 41, nr 9, s. 850-861, artikel-id sxad049Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to e.g., distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation towards hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.

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  • 37.
    Mathew, Sumi
    Högskolan i Skövde, Institutionen för kommunikation och information.
    A method to identify the non-coding RNA gene for U1 RNA in species in which it has not yet been found2007Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Background

    Non coding RNAs are the RNA molecules that do not code for proteins but play structural, catalytic or regulatory roles in the organisms in which they are found. These RNAs generally conserve their secondary structure more than their primary sequence. It is possible to look for protein coding genes using sequence signals like promoters, terminators, start and stop codons etc. However, this is not the case with non coding RNAs since these signals are weakly conserved in them. Hence the situation with non coding RNAs is more challenging. Therefore a protocol is devised to identify U1 RNA in species not previously known to have it.

    Results

    It is sufficient to use the covariance models to identify non coding RNAs but they are very slow and hence a filtering step is needed before using the covariance models to reduce the search space for identifying these genes. The protocol for identifying U1 RNA genes employs for the filtering a pattern matcher RNABOB that can conduct secondary structure pattern searches. The descriptor for RNABOB is made automatically such that it can also represent the bulges and interior loops in helices of RNA. The protocol is compared with the Rfam and Weinberg & Ruzzo approaches and has been able to identify new U1 RNA homologues in the Apicomplexan group where it has not previously been found.

    Conclusions

    The method has been used to identify the gene for U1 RNA in certain species in which it has not been detected previously. The identified genes may be further analyzed by wet laboratory techniques for the confirmation of their existence.

    4

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  • 38.
    Muhammad, Ashfaq
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Design and Development of a Database for the Classification of Corynebacterium glutamicum Genes, Proteins, Mutants and Experimental Protocols2006Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Coryneform bacteria are largely distributed in nature and are rod like, aerobic soil bacteria capable of growing on a variety of sugars and organic acids. Corynebacterium glutamicum is a nonpathogenic species of Coryneform bacteria used for industrial production of amino acids. There are three main publicly available genome annotations, Cg, Cgl and NCgl for C. glutamicum. All these three annotations have different numbers of protein coding genes and varying numbers of overlaps of similar genes. The original data is only available in text files. In this format of genome data, it was not easy to search and compare the data among different annotations and it was impossible to make an extensive multidimensional customized formal search against different protein parameters. Comparison of all genome annotations for construction deletion, over-expression mutants, graphical representation of genome information, such as gene locations, neighboring genes, orientation (direct or complementary strand), overlapping genes, gene lengths, graphical output for structure function relation by comparison of predicted trans-membrane domains (TMD) and functional protein domains protein motifs was not possible when data is inconsistent and redundant on various publicly available biological database servers. There was therefore a need for a system of managing the data for mutants and experimental setups. In spite of the fact that the genome sequence is known, until now no databank providing such a complete set of information has been available. We solved these problems by developing a standalone relational database software application covering data processing, protein-DNA sequence extraction and

    management of lab data. The result of the study is an application named, CORYNEBASE, which is a software that meets our aims and objectives.

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  • 39.
    Narayanan, Ajit
    et al.
    School of Engineering and Computer Sciences, University of Exeter, UK.
    Keedwell, Edward C
    School of Engineering and Computer Sciences, University of Exeter, UK.
    Olsson, Björn
    Högskolan i Skövde, Institutionen för datavetenskap.
    Artificial intelligence techniques for bioinformatics2002Ingår i: Applied Bioinformatics, ISSN 1175-5636, Vol. 1, nr 4, s. 191-222Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    This review provides an overview of the ways in which techniques from artificial intelligence (AI) can be usefully employed in bioinformatics, both for modelling biological data and for making new discoveries. The paper covers three techniques: symbolic machine learning approaches (nearest neighbour and identification tree techniques), artificial neural networks and genetic algorithms. Each technique is introduced and supported with examples taken from the bioinformatics literature. These examples include folding prediction, viral protease cleavage prediction, classification, multiple sequence alignment and microarray gene expression analysis.

  • 40.
    Naswa, Sudhir
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Representation of Biochemical Pathway Models: Issues relating conversion of model representation from SBML to a commercial tool2005Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Background: Computational simulation of complex biological networks lies at the heart of systems biology since it can confirm the conclusions drawn by experimental studies of biological networks and guide researchers to produce fresh hypotheses for further experimental validation. Since this iterative process helps in development of more realistic system models a variety of computational tools have been developed. In the absence of a common format for representation of models these tools were developed in different formats. As a result these tools became unable to exchange models amongst them, leading to development of SBML, a standard exchange format for computational models of biochemical networks. Here the formats of SBML and one of the commercial tools of systems biology are being compared to study the issues which may arise during conversion between their respective formats. A tool StoP has been developed to convert the format of SBML to the format of the selected tool.

    Results: The basic format of SBML representation which is in the form of listings of various elements of a biochemical reaction system differs from the representation of the selected tool which is location oriented. In spite of this difference the various components of biochemical pathways including multiple compartments, global parameters, reactants, products, modifiers, reactions, kinetic formulas and reaction parameters could be converted from the SBML representation to the representation of the selected tool. The MathML representation of the kinetic formula in an SBML model can be converted to the string format of the selected tool. Some features of the SBML are not present in the selected tool. Similarly, the ability of the selected tool to declare parameters for locations, which are global to those locations and their children, is not present in the SBML.

    Conclusions: Differences in representations of pathway models may include differences in terminologies, basic architecture, differences in capabilities of software’s, and adoption of different standards for similar things. But the overall similarity of domain of pathway models enables us to interconvert these representations. The selected tool should develop support for unit definitions, events and rules. Development of facility for parameter declaration at compartment level by SBML and facility for function declaration by the selected tool is recommended.

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  • 41.
    Nordström, Rickard
    Högskolan i Skövde, Institutionen för datavetenskap.
    3DPOPS: From carbohydrate sequence to 3D structure2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    In this project a web-based system called 3DPOPS have been designed, developed and implemented. The system creates initial 3D structures of oligosaccharides according to user input data and is intended to be integrated with an automatized 3D prediction system for saccharides. The web interface uses a novel approach with a dynamically updated graphical representation of the input carbohydrate. The interface is embedded in a web page as a Java applet. Both expert and novice users needs are met by informative messages, a familiar concept and a dynamically updated graphical user interface in which only valid input can be created.

    A set of test sequences was collected from the CarbBank database. An initial structure to each sequence could be created. All contained the information necessary to serve as starting points in a conformation search carried out by a 3D prediction system for carbohydrates.

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  • 42.
    Nyberg, Lena K.
    et al.
    Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Quaderi, Saair
    Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden / Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Emilsson, Gustav
    Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden / Department of Applied Physics, Chalmers University of Technology, Gothenburg, Sweden.
    Karami, Nahid
    Department of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Lagerstedt, Erik
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Müller, Vilhelm
    Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Noble, Charleston
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Hammarberg, Susanna
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Nilsson, Adam N.
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Sjöberg, Fei
    Department of Infectious Diseases, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Fritzsche, Joachim
    Department of Applied Physics, Chalmers University of Technology, Gothenburg, Sweden.
    Kristiansson, Erik
    Department of Mathematical Sciences, Chalmers University of Technology, University of Gothenburg, Gothenburg, Sweden.
    Sandegren, Linus
    Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
    Ambjörnsson, Tobias
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Westerlund, Fredrik
    Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Rapid identification of intact bacterial resistance plasmids via optical mapping of single DNA molecules2016Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 6, artikel-id 30410Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The rapid spread of antibiotic resistance - currently one of the greatest threats to human health according to WHO - is to a large extent enabled by plasmid-mediated horizontal transfer of resistance genes. Rapid identification and characterization of plasmids is thus important both for individual clinical outcomes and for epidemiological monitoring of antibiotic resistance. Toward this aim, we have developed an optical DNA mapping procedure where individual intact plasmids are elongated within nanofluidic channels and visualized through fluorescence microscopy, yielding barcodes that reflect the underlying sequence. The assay rapidly identifies plasmids through statistical comparisons with barcodes based on publicly available sequence repositories and also enables detection of structural variations. Since the assay yields holistic sequence information for individual intact plasmids, it is an ideal complement to next generation sequencing efforts which involve reassembly of sequence reads from fragmented DNA molecules. The assay should be applicable in microbiology labs around the world in applications ranging from fundamental plasmid biology to clinical epidemiology and diagnostics.

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  • 43.
    Olsson, Elin
    Högskolan i Skövde, Institutionen för datavetenskap.
    Deriving Genetic Networks Using Text Mining2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    On the Internet an enormous amount of information is available that is represented in an unstructured form. The purpose with a text mining tool is to collect this information and present it in a more structured form. In this report text mining is used to create an algorithm that searches abstracts available from PubMed and finds specific relationships between genes that can be used to create a network. The algorithm can also be used to find information about a specific gene. The network created by Mendoza et al. (1999) was verified in all the connections but one using the algorithm. This connection contained implicit information. The results suggest that the algorithm is better at extracting information about specific genes than finding connections between genes. One advantage with the algorithm is that it can also find connections between genes and proteins and genes and other chemical substances.

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  • 44.
    Omoghene, Hope
    Högskolan i Skövde, Institutionen för biovetenskap.
    Computational detection of human papilloma virus in the cervical cancer genome2023Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Global research and development have witnessed new horizons in technological advancements, especially in the use of new-generation bioinformatic tools to solve human needs. Cervical cancer, caused by a sexually transmitted virus like human papillomavirus (HPV), is one of the most common cancers threatening women's health. The main aim of the study is to evaluate existing Next-generation pipelines for detection of HPV in cervical cancer. The method includes data retrieval, which involves careful selection and downloading of 30 metagenomic data (in FASTA-Q format) from the Human Microbiome Project database. The implementation phase of the study involved setting up and configuring the virus detection tools (HPViewer, VirusSeq and VirusFinder 2.0). All the tools were run on default settings to analyze the metagenome samples using the instructions provided by their authors. The result showed that the tools detected HPV. The HPViewer demonstrated a higher level of HPV detection, followed by VirusSeq and then VirusFinder 2. The HPViewer had the shortest run time, completing an analysis in 24.1 seconds, followed by VirusFinder 2 in 208 seconds and VirusSeq took 4200 seconds (1 hour, 10 minutes to run). HPViewer demonstrated an outstanding sensitivity of 100%, VirusFinder 2 (45.5 %) and VirusSeq (63.6%). In conclusion, the present study underscored the trade-offs between speed, accuracy, and resource consumption between bioinformatics tools for HPV detection. Each of the tools exhibited unique strengths and limitations; however, they provided valuable options for HPV detection.

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  • 45.
    Pasalic, Zlatana
    Högskolan i Skövde, Institutionen för datavetenskap.
    Evaluation of search models for Molecular Replacement using MolRep2002Självständigt arbete på grundnivå (kandidatexamen)Studentuppsats
    Abstract [en]

    he aim of this study is to use several homology models of different completeness and accuracy and to evaluate them as search models for Molecular Replacement (MR).Three structural groups are evaluated: α-, β- and α/β- group. From every group one template structure and a couple of search models are selected. The search models are manipulated and evaluated. B-factor manipulation, side chain removal and homology modelling are the ways the search models are manipulated. This work shows that B-factor manipulation do not improve the search models. The work also shows that removing the side chains is not improving the search models. Finally the work shows that homology modelling did not model better search models.

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  • 46.
    Pohl, Matin
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Using an ontology to enhance metabolic or signaling pathway comparisions by biological and chemical knowledge2006Studentuppsats
    Abstract [en]

    Motivation:

    As genome-scale efforts are ongoing to investigate metabolic networks of miscellaneous organisms the amount of pathway data is growing. Simultaneously an increasing amount of gene expression data from micro arrays becomes available for reverse engineering, delivering e.g. hypothetical regulatory pathway data. To avoid outgrowing of data and keep control of real new informations the need of analysis tools arises. One vital task is the comparison of pathways for detection of similar functionalities, overlaps, or in case of reverse engineering, detection of known data corroborating a hypothetical pathway. A comparison method using ontological knowledge about molecules and reactions will feature a more biological point of view which graph theoretical approaches missed so far. Such a comparison attempt based on an ontology is described in this report.

    Results:

    An algorithm is introduced that performs a comparison of pathways component by component. The method was performed on two selected databases and the results proved it to be not satisfying using it as stand-alone method. Further development possibilities are suggested and steps toward an integrated method using several approaches are recommended.

    Availability:

    The source code, used database snapshots and pictures can be requested from the author.

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  • 47.
    Poudel, Sagar
    Högskolan i Skövde, Institutionen för kommunikation och information.
    GPCR-Directed Libraries for High Throughput Screening2006Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Guanine nucleotide binding protein (G-protein) coupled receptors (GPCRs), the largest receptor family, is enormously important for the pharmaceutical industry as they are the target of 50-60% of all existing medicines. Discovery of many new GPCR receptors by the “human genome project”, open up new opportunities for developing novel therapeutics. High throughput screening (HTS) of chemical libraries is a well established method for finding new lead compounds in drug discovery. Despite some success this approach has suffered from the near absence of more focused and specific targeted libraries. To improve the hit rates and to maximally exploit the full potential of current corporate screening collections, in this thesis work, identification and analysis of the critical drug-binding positions within the GPCRs were done, based on their overall sequence, their transmembrane regions and their drug binding fingerprints. A proper classification based on drug binding fingerprints on the basis for a successful pharmacophore modelling and virtual screening were done, which facilities in the development of more specific and focused targeted libraries for HTS.

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  • 48.
    Rahpeymai, Neda
    Högskolan i Skövde, Institutionen för datavetenskap.
    Data Mining with Decision Trees in the Gene Logic Database: A Breast Cancer Study2002Självständigt arbete på avancerad nivå (magisterexamen)Studentuppsats
    Abstract [en]

    Data mining approaches have been increasingly used in recent years in order to find patterns and regularities in large databases. In this study, the C4.5 decision tree approach was used for mining of Gene Logic database, containing biological data. The decision tree approach was used in order to identify the most relevant genes and risk factors involved in breast cancer, in order to separate healthy patients from breast cancer patients in the data sets used. Four different tests were performed for this purpose. Cross validation was performed, for each of the four tests, in order to evaluate the capacity of the decision tree approaches in correctly classifying ‘new’ samples. In the first test, the expression of 108 breast related genes, shown in appendix A, for 75 patients were used as input to the C4.5 algorithm. This test resulted in a decision tree containing only four genes considered to be the most relevant in order to correctly classify patients. Cross validation indicates an average accuracy of 89% in classifying ‘new’ samples. In the second test, risk factor data was used as input. The cross validation result shows an average accuracy of 87% in classifying ‘new’ samples. In the third test, both gene expression data and risk factor data were put together as one input. The cross validation procedure for this approach again indicates an average accuracy of 87% in classifying ‘new’ samples. In the final test, the C4.5 algorithm was used in order to indicate possible signalling pathways involving the four genes identified by the decision tree based on only gene expression data. In some of cases, the C4.5 algorithm found trees suggesting pathways which are supported by the breast cancer literature. Since not all pathways involving the four putative breast cancer genes are known yet, the other suggested pathways should be further analyzed in order to increase their credibility.

    In summary, this study demonstrates the application of decision tree approaches for the identification of genes and risk factors relevant for the classification of breast cancer patients

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  • 49.
    Rahpeymai, Neda
    et al.
    Högskolan i Skövde, Institutionen för datavetenskap.
    Olsson, Björn
    Högskolan i Skövde, Institutionen för datavetenskap.
    Andersson, Magnus L.
    Astrazeneca R&D, Sweden.
    Microarray-based diagnosis of breast cancer using decision trees2003Ingår i: / [ed] K. Chen; S. Chen; H. Cheng; D. Chiu; S. Das; R. Duro; Z. Jiang; N. Kasabov; Etienne Kerre; H. Leong; Q. Li; M. Lu; M. Romay; D. Ventura; P. Wang; J. Wu, Durham, NC, USA: Association for Intelligent Machinery Inc. , 2003, s. 978-982Konferensbidrag (Refereegranskat)
    Abstract [en]

    We apply the decision tree algorithm C4.5 to gene expression data in order to induce decision trees for identification of breast cancer patients. Using expression data from 108 known breast cancer-related genes for 75 patients with various diseases of the breast, we are able to induce decision trees with 89% accuracy in separating cancer from non-cancer patients in a cross-validation test. We also show that by inducing a separate decision tree for each cancer-related gene, and using the expression level of the individual gene as the decision variable, it is possible to obtain decision trees which aid the understanding of signaling pathways involved in breast cancer. In addition, we also show that the C4.5 algorithm is able to identify key breast cancer genes when decision trees are induced on expression data sets containing randomly selected genes. This result indicates that it is possible to make biological discoveries when applying decision tree algorithms to large sets of gene expression data in diseases where the genetic basis is not well characterised.

  • 50.
    Sentausa, Erwin
    Högskolan i Skövde, Institutionen för kommunikation och information.
    Time course simulation replicability of SBML-supporting biochemical network simulation tools2006Självständigt arbete på avancerad nivå (magisterexamen), 20 poäng / 30 hpStudentuppsats
    Abstract [en]

    Background: Modelling and simulation are important tools for understanding biological systems. Numerous modelling and simulation software tools have been developed for integrating knowledge regarding the behaviour of a dynamic biological system described in mathematical form. The Systems Biology Markup Language (SBML) was created as a standard format for exchanging biochemical network models among tools. However, it is not certain yet whether actual usage and exchange of SBML models among the tools of different purpose and interfaces is assessable. Particularly, it is not clear whether dynamic simulations of SBML models using different modelling and simulation packages are replicable.

    Results: Time series simulations of published biological models in SBML format are performed using four modelling and simulation tools which support SBML to evaluate whether the tools correctly replicate the simulation results. Some of the tools do not successfully integrate some models. In the time series output of the successful

    simulations, there are differences between the tools.

    Conclusions: Although SBML is widely supported among biochemical modelling and simulation tools, not all simulators can replicate time-course simulations of SBML models exactly. This incapability of replicating simulation results may harm the peer-review process of biological modelling and simulation activities and should be addressed accordingly, for example by specifying in the SBML model the exact algorithm or simulator used for replicating the simulation result.

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