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Identification of personalized multi-omic disease modules in asthma
University of Skövde, School of Bioscience. (Translational Bioinformatics, Linköping University)
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 40 credits / 60 HE creditsStudent thesis
Abstract [en]

Asthma is a respiratory syndrome associated with airflow limitation, bronchial hyperresponsiveness and inflammation of the airways in the lungs. Despite the ongoing research efforts, the outstanding heterogeneity displayed by the multiple forms in which this condition presents often hampers the attempts to determine and classify the phenotypic and endotypic biological structures at play, even when considering a limited assembly of asthmatic subjects. To increase our understanding of the molecular mechanisms and functional pathways that govern asthma from a systems medicine perspective, a computational workflow focused on the identification of personalized transcriptomic modules from the U-BIOPRED study cohorts, by the use of the novel MODifieR integrated R package, was designed and applied. A feature selection of candidate asthma biomarkers was implemented, accompanied by the detection of differentially expressed genes across sample categories, the production of patient-specific gene modules and the subsequent construction of a set of core disease modules of asthma, which were validated with genomic data and analyzed for pathway and disease enrichment. The results indicate that the approach utilized is able to reveal the presence of components and signaling routes known to be crucially involved in asthma pathogenesis, while simultaneously uncovering candidate genes closely linked to the latter. The present project establishes a valuable pipeline for the module-driven study of asthma and other related conditions, which can provide new potential targets for therapeutic intervention and contribute to the development of individualized treatment strategies.

Place, publisher, year, edition, pages
2018. , p. 84
Keywords [en]
modules, asthma, personalized, systems biology, molecular biology, transcriptomics, genomics, networks, disease, biomarker, microarray, individualized, endotype, phenotype
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:his:diva-15987OAI: oai:DiVA.org:his-15987DiVA, id: diva2:1233438
Subject / course
Systems Biology
Educational program
Molecular Biotechnology - Master's Programme, 120 ECTS
Supervisors
Examiners
Available from: 2018-08-27 Created: 2018-07-18 Last updated: 2018-08-27Bibliographically approved

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Master_Thesis_David_Martinez(4418 kB)144 downloads
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
  • rtf