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Ciruvis: a web-based tool for rule networks and interaction detection using rule-based classifiers
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
2014 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 15, 139- p.Article in journal (Refereed) Published
Abstract [en]

Background: The use of classification algorithms is becoming increasingly important for the field of computational biology. However, not only the quality of the classification, but also its biological interpretation is important. This interpretation may be eased if interacting elements can be identified and visualized, something that requires appropriate tools and methods. Results: We developed a new approach to detecting interactions in complex systems based on classification. Using rule-based classifiers, we previously proposed a rule network visualization strategy that may be applied as a heuristic for finding interactions. We now complement this work with Ciruvis, a web-based tool for the construction of rule networks from classifiers made of IF-THEN rules. Simulated and biological data served as an illustration of how the tool may be used to visualize and interpret classifiers. Furthermore, we used the rule networks to identify feature interactions, compared them to alternative methods, and computationally validated the findings. Conclusions: Rule networks enable a fast method for model visualization and provide an exploratory heuristic to interaction detection. The tool is made freely available on the web and may thus be used to aid and improve rule-based classification.

Place, publisher, year, edition, pages
2014. Vol. 15, 139- p.
Keyword [en]
Visualization, Rules, Interactions, Interaction detection, Classification, Rule-based classification
National Category
Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:uu:diva-228027DOI: 10.1186/1471-2105-15-139ISI: 000336679600001OAI: oai:DiVA.org:uu-228027DiVA: diva2:731856
Available from: 2014-07-02 Created: 2014-07-02 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Rule-based Models of Transcriptional Regulation and Complex Diseases: Applications and Development
Open this publication in new window or tab >>Rule-based Models of Transcriptional Regulation and Complex Diseases: Applications and Development
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

As we gain increased understanding of genetic disorders and gene regulation more focus has turned towards complex interactions. Combinations of genes or gene and environmental factors have been suggested to explain the missing heritability behind complex diseases. Furthermore, gene activation and splicing seem to be governed by a complex machinery of histone modification (HM), transcription factor (TF), and DNA sequence signals. This thesis aimed to apply and develop multivariate machine learning methods for use on such biological problems. Monte Carlo feature selection was combined with rule-based classification to identify interactions between HMs and to study the interplay of factors with importance for asthma and allergy.

Firstly, publicly available ChIP-seq data (Paper I) for 38 HMs was studied. We trained a classifier for predicting exon inclusion levels based on the HMs signals. We identified HMs important for splicing and illustrated that splicing could be predicted from the HM patterns. Next, we applied a similar methodology on data from two large birth cohorts describing asthma and allergy in children (Paper II). We identified genetic and environmental factors with importance for allergic diseases which confirmed earlier results and found candidate gene-gene and gene-environment interactions.

In order to interpret and present the classifiers we developed Ciruvis, a web-based tool for network visualization of classification rules (Paper III). We applied Ciruvis on classifiers trained on both simulated and real data and compared our tool to another methodology for interaction detection using classification. Finally, we continued the earlier study on epigenetics by analyzing HM and TF signals in genes with or without evidence of bidirectional transcription (Paper IV). We identified several HMs and TFs with different signals between unidirectional and bidirectional genes. Among these, the CTCF TF was shown to have a well-positioned peak 60-80 bp upstream of the transcription start site in unidirectional genes.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 69 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1167
Keyword
Histone modification, Transcription factor, Transcriptional regulation, Next-generation sequencing, Feature selection, Machine learning, Rule-based classification, Asthma, Allergy
National Category
Bioinformatics and Systems Biology Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
urn:nbn:se:uu:diva-230159 (URN)978-91-554-9005-8 (ISBN)
Public defence
2014-10-03, BMC C8:301, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2014-09-12 Created: 2014-08-19 Last updated: 2015-01-22

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Bornelöv, SusanneKomorowski, Jan

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