# An automatic integrative method for learninginterpretable communities of biological pathways
### Nicasia Beebe-Wang, Ayse B. Dincer, and Su-In Lee
##### Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle
Although biological pathways are essential forinterpreting results from computational biologystudies, the growing number of pathway databasesmakes it difficult to perform pathway analysis.Our study seeks to reconcile pathways from dif-ferent databases and reduce pathway redundancyby revealing informative groups with distinct bio-logical functions. Uniquely applying the Louvaincommunity detection algorithm to a network of4,847 pathways from KEGG, REACTOME andGene Ontology databases, we identify 35 distinctcommunities of pathways and show that thesecommunities are consistent with expert-curatedpathway categories. Further, we develop an algo-rithm to automatically annotate each communitybased on member pathways’ names. By learn-ing informative categories, we progress towards atool that computational biologists can use to moreefficiently interpret their biological findings.
