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Bae, J., Karlsson, A., Mellin, J., Ståhl, N. & Torra, V. (2019). Complex Data Analysis. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 157-169). Springer
Open this publication in new window or tab >>Complex Data Analysis
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2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 157-169Chapter in book (Refereed)
Abstract [en]

Data science applications often need to deal with data that does not fit into the standard entity-attribute-value model. In this chapter we discuss three of these other types of data. We discuss texts, images and graphs. The importance of social media is one of the reason for the interest on graphs as they are a way to represent social networks and, in general, any type of interaction between people. In this chapter we present examples of tools that can be used to extract information and, thus, analyze these three types of data. In particular, we discuss topic modeling using a hierarchical statistical model as a way to extract relevant topics from texts, image analysis using convolutional neural networks, and measures and visual methods to summarize information from graphs.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Computer Sciences Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL); Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-16811 (URN)10.1007/978-3-319-97556-6_9 (DOI)000464719500010 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2019-09-30Bibliographically approved
Ståhl, N., Falkman, G., Karlsson, A., Mathiason, G. & Boström, J. (2019). Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design. Journal of Chemical Information and Modeling, 59(7), 3166-3176
Open this publication in new window or tab >>Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design
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2019 (English)In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 59, no 7, p. 3166-3176Article in journal (Refereed) Published
Abstract [en]

In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modeled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improving these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output toward structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid and more than a third satisfy the targeted objectives, while there were none in the initial set.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2019
Keywords
algorithms, molecules
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-17503 (URN)10.1021/acs.jcim.9b00325 (DOI)000477074900010 ()31273995 (PubMedID)2-s2.0-85070180995 (Scopus ID)
Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2019-11-13Bibliographically approved
Ståhl, N., Falkman, G., Karlsson, A., Mathiason, G. & Boström, J. (2019). Improving the use of deep convolutional neural networks for the prediction of molecular properties. In: Florentino Fdez-Riverola, Mohd Saberi Mohamad, Miguel Rocha, Juan F. De Paz, Pascual González (Ed.), Practical Applications of Computational Biology and Bioinformatics, 12th International Conference: . Paper presented at PACBB2018: International Conference on Practical Applications of Computational Biology & Bioinformatics, Toledo, June 20-22, 2018 (pp. 71-79). Cham: Springer, 803
Open this publication in new window or tab >>Improving the use of deep convolutional neural networks for the prediction of molecular properties
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2019 (English)In: Practical Applications of Computational Biology and Bioinformatics, 12th International Conference / [ed] Florentino Fdez-Riverola, Mohd Saberi Mohamad, Miguel Rocha, Juan F. De Paz, Pascual González, Cham: Springer, 2019, Vol. 803, p. 71-79Conference paper, Published paper (Refereed)
Abstract [en]

We present a flexible deep convolutional neural network method for the analyse of arbitrary sized graph structures representing molecules. The method makes use of RDKit, an open-source cheminformatics software, allowing the incorporation of any global molecular (such as molecular charge) and local (such as atom type) information. We evaluate the method on the Side Effect Resource (SIDER) v4.1 dataset and show that it significantly outperforms another recently proposed method based on deep convolutional neural networks. We also reflect on how different types of information and input data affect the predictive power of our model. This reflection highlights several open problems that should be solved to further improve the use of deep learning within cheminformatics.

Place, publisher, year, edition, pages
Cham: Springer, 2019
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 803
Keywords
drug discovery, graph convolutional neural network, molecular property prediction, bioinformatics, convolution, neural networks, open source software, open systems, cheminformatics, convolutional neural network, deep convolutional neural networks, graph structures, molecular charge, molecular properties, predictive power, deep neural networks
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16230 (URN)10.1007/978-3-319-98702-6_9 (DOI)000468071900009 ()2-s2.0-85052956812 (Scopus ID)978-3-319-98701-9 (ISBN)978-3-319-98702-6 (ISBN)
Conference
PACBB2018: International Conference on Practical Applications of Computational Biology & Bioinformatics, Toledo, June 20-22, 2018
Available from: 2018-09-25 Created: 2018-09-25 Last updated: 2019-06-07Bibliographically approved
Duarte, D. & Ståhl, N. (2019). Machine Learning: A Concise Overview. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 27-58). Springer
Open this publication in new window or tab >>Machine Learning: A Concise Overview
2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 27-58Chapter in book (Refereed)
Abstract [en]

Machine learning is a sub-field of computer science that aims to make computers learn. It is a simple view of this field, but since the first computer was built, we have wondered whether or not they can learn as we do.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16782 (URN)10.1007/978-3-319-97556-6_3 (DOI)000464719500004 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-16 Created: 2019-04-16 Last updated: 2019-09-30Bibliographically approved
Ståhl, N., Mathiason, G., Falkman, G. & Karlsson, A. (2019). Using recurrent neural networks with attention for detecting problematic slab shapes in steel rolling. Applied Mathematical Modelling, 70, 365-377
Open this publication in new window or tab >>Using recurrent neural networks with attention for detecting problematic slab shapes in steel rolling
2019 (English)In: Applied Mathematical Modelling, ISSN 0307-904X, E-ISSN 1872-8480, Vol. 70, p. 365-377Article in journal (Refereed) Published
Abstract [en]

The competitiveness in the manufacturing industry raises demands for using recent data analysis algorithms for manufacturing process development. Data-driven analysis enables extraction of novel knowledge from already existing sensors and data, which is necessary for advanced manufacturing process refinement involving aged machinery. Improved data analysis enables factories to stay competitive against newer factories, but without any hefty investment. In large manufacturing operations, the dependencies between data are highly complex and therefore very difficult to analyse manually. This paper applies a deep learning approach, using a recurrent neural network with long short term memory cells together with an attention mechanism to model the dependencies between the measured product shape, as measured before the most critical manufacturing operation, and the final product quality. Our approach predicts the ratio of flawed products already before the critical operation with an AUC-ROC score of 0.85, i.e., we can detect more than 80 % of all flawed products while having less than 25 % false positive predictions (false alarms). In contrast to previous deep learning approaches, our method shows how the recurrent neural network reasons about the input shape, using the attention mechanism to point out which parts of the product shape that have the highest influence on the predictions. Such information is crucial for both process developers, in order to understand and improve the process, and for process operators who can use the information to learn how to better trust the predictions and control the process.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Attention mechanism, Recurrent Neural Networks, Interpretable AI, Steel Rolling
National Category
Computer Sciences
Research subject
INF301 Data Science; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16588 (URN)10.1016/j.apm.2019.01.027 (DOI)000468714000021 ()2-s2.0-85060941525 (Scopus ID)
Projects
DataFlow
Funder
Vinnova, 2017-01531
Available from: 2019-01-29 Created: 2019-01-29 Last updated: 2019-07-10Bibliographically approved
Ståhl, N., Falkman, G., Mathiason, G. & Karlsson, A. (2018). A self-organizing ensemble of deep neural networks for the classification of data from complex processes. In: Medina, J., Ojeda-Aciego, M., Verdegay, J.L., Perfilieva, I., Bouchon-Meunier, B., Yager, R.R. (Ed.), INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: APPLICATIONS, IPMU 2018, PT III: . Paper presented at 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 11 - Jun 15, 2018, Cadiz, Spain (pp. 248-259). , 855
Open this publication in new window or tab >>A self-organizing ensemble of deep neural networks for the classification of data from complex processes
2018 (English)In: INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: APPLICATIONS, IPMU 2018, PT III / [ed] Medina, J., Ojeda-Aciego, M., Verdegay, J.L., Perfilieva, I., Bouchon-Meunier, B., Yager, R.R., 2018, Vol. 855, p. 248-259Conference paper, Published paper (Refereed)
Abstract [en]

We present a new self-organizing algorithm for classification of a data that combines and extends the strengths of several common machine learning algorithms, such as algorithms in self-organizing neural networks, ensemble methods and deep neural networks. The increased expression power is combined with the explanation power of self-organizing networks. Our algorithm outperforms both deep neural networks and ensembles of deep neural networks. For our evaluation case, we use production monitoring data from a complex steel manufacturing process, where data is both high-dimensional and has many nonlinear interdependencies. In addition to the improved prediction score, the algorithm offers a new deep-learning based approach for how computational resources can be focused in data exploration, since the algorithm points out areas of the input space that are more challenging to learn.

Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937
Keywords
artificial neural networks, complex processes, ensemble methods, self organisation
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-15008 (URN)10.1007/978-3-319-91479-4_21 (DOI)000481660700021 ()2-s2.0-85048061876 (Scopus ID)978-3-319-91479-4 (ISBN)978-3-319-91478-7 (ISBN)
Conference
17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 11 - Jun 15, 2018, Cadiz, Spain
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2019-09-05Bibliographically approved
Ståhl, N., Falkman, G., Karlsson, A., Mathiason, G. & Boström, J. (2018). Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data. Journal of Integrative Bioinformatics, 16(1)
Open this publication in new window or tab >>Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data
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2018 (English)In: Journal of Integrative Bioinformatics, E-ISSN 1613-4516, Vol. 16, no 1Article in journal (Refereed) Published
Abstract [en]

We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables the incorporation of any global molecular (such as molecular charge and molecular weight) and local (such as atom hybridization and bond orders) information. In this paper, we show that this method significantly outperforms another recently proposed method based on deep convolutional neural networks on several datasets that are studied. Several best practices for training deep convolutional neural networks on chemical datasets are also highlighted within the article, such as how to select the information to be included in the model, how to prevent overfitting and how unbalanced classes in the data can be handled.

Place, publisher, year, edition, pages
De Gruyter Open, 2018
Keywords
Molecular property prediction, Deep learning, Unbalanced data, Side effects prediction
National Category
Computer Sciences Medicinal Chemistry
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16480 (URN)10.1515/jib-2018-0065 (DOI)000459384300004 ()30517077 (PubMedID)2-s2.0-85061622864 (Scopus ID)
Available from: 2018-12-09 Created: 2018-12-09 Last updated: 2019-06-13Bibliographically approved
Ståhl, N. (2017). Challenges and opportunities of analysing complex data using deep learning.
Open this publication in new window or tab >>Challenges and opportunities of analysing complex data using deep learning
2017 (English)Report (Other academic)
Publisher
p. 31
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14574 (URN)
Note

Research proposal, PhD programme, University of Skövde

Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2018-06-11Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2128-7090

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