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Comparing consensus modules using S2B and MODifieR
University of Skövde, School of Bioscience.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

It is currently understood that diseases are typically not caused by rogue errors in genetics but have both molecular and environmental causes from myriad overlapping interactions within an interactome. Genetic errors, such as that seen by a single-nucleotide polymorphism can lead to a dysfunctional cell, which in turn can lead to systemic disruptions that result in disease phenotypes. Perturbations within the interactome, as can be caused by many such errors, can be organized into a pathophenotype, or “disease module”. Disease modules are sets of correlated variables that can represent many of a disease’s activities with subgraphs of nodes and edges. Many methods for inferring disease modules are available today, but the results each one yields is not only variable between methods but also across datasets and trial attempts. In this study, several such inference methods for deriving disease modules are evaluated by combining them to create “consensus” modules. The method of focus is Double-Specific Betweenness (S2B), which uses betweenness centrality across separate diseases to derive new modules. This study, however, uses S2B to combine the results of independent inference methods rather than separate diseases to derive new modules. Pre-processed asthma and arthritis data are compared using various combinations of inference methods. The performance of each result is validated using Pathway Scoring Algorithm. The results of this study suggest that combining methods of inference using MODifieR or S2B may be beneficial for deriving meaningful disease modules.

Place, publisher, year, edition, pages
2019. , p. 29
Keywords [en]
S2B, MODifieR, specific betweenness, betweenness centrality, disease module, module inference, asthma, arthritis
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:his:diva-16760OAI: oai:DiVA.org:his-16760DiVA, id: diva2:1303151
Subject / course
Bioinformatics
Educational program
Bioinformatics - Master’s Programme
Supervisors
Examiners
Available from: 2019-04-12 Created: 2019-04-09 Last updated: 2019-04-12Bibliographically approved

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CiteExportLink to record
Permanent link

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