# An automatic integrative method for learninginterpretable communities of biological pathways
### Nicasia Beebe-Wang, Ayse B. Dincer, and Su-In Lee
### Nicasia Beebe-Wang*, Ayse B. Dincer*, and Su-In Lee
##### Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle
###### * Equal contribution
Although biological pathways are essential for interpreting results from computational biology studies, the growing number of pathway databases makes it difficult to perform pathway analysis. Our study seeks to reconcile pathways from different databases and reduce pathway redundancy by revealing informative groups with distinct biological functions. Uniquely applying the Louvain community detection algorithm to a network of 4,847 pathways from KEGG, REACTOME and Gene Ontology databases, we identify 35 distinct communities of pathways and show that these communities are consistent with expert-curated pathway categories. Further, we develop an algorithm to automatically annotate each community based on member pathways’ names. By learning informative categories, we progress towards a tool that computational biologists can use to more efficiently interpret their biological findings.
