Prior knowledge guided active modules identification: An integrated multi-objective approach

Research output: Contribution to journalArticlepeer-review

Authors

Colleges, School and Institutes

External organisations

  • Xidian University

Abstract

Background: Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. Methods: A prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules. Results: Application of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation. Conclusions: Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance.

Bibliographic note

Funding Information: Not applicable., This paper was supported by European Union Seventh Framework Programme (FP7 / 2007-2013; grant agreement number NMP4-LA-2013- 310451). The publication costs for this article was also funded by European Union Seventh Framework Programme (FP7 / 2007-2013; grant agreement number NMP4-LA-2013-310451). Publisher Copyright: © 2017 The Author(s). Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

Details

Original languageEnglish
Article number8
JournalBMC systems biology
Volume11
Publication statusPublished - 14 Mar 2017

Keywords

  • Active module identification, Multi-objective evolutionary algorithm, Prior knowlege