Open AccessWIPER: Weighted in‐Path Edge Ranking for biomolecular association networks
Author(s) -
Yue Zongliang,
Nguyen Thanh,
Zhang Eric,
Zhang Jianyi,
Chen Jake Y.
Publication year - 2019
Publication title -
quantitative biology
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.1007/s40484-019-0180-y
Subject(s) - betweenness centrality , ranking (information retrieval) , path (computing) , enhanced data rates for gsm evolution , computer science , centrality , tree traversal , biological network , computational biology , data mining , fyn , bioinformatics , artificial intelligence , biology , algorithm , mathematics , computer network , genetics , signal transduction , combinatorics , proto oncogene tyrosine protein kinase src
Background In network biology researchers generate biomolecular networks with candidate genes or proteins experimentally‐derived from high‐throughput data and known biomolecular associations. Current bioinformatics research focuses on characterizing candidate genes/proteins, or nodes, with network characteristics, e.g. , betweenness centrality. However, there have been few research reports to characterize and prioritize biomolecular associations (“edges”), which can represent gene regulatory events essential to biological processes. Method We developed Weighted In‐Path Edge Ranking (WIPER), a new computational algorithm which can help evaluate all biomolecular interactions/associations (“edges”) in a network model and generate a rank order of every edge based on their in‐path traversal scores and statistical significance test result. To validate whether WIPER worked as we designed, we tested the algorithm on synthetic network models. Results Our results showed WIPER can reliably discover both critical “well traversed in‐path edges”, which are statistically more traversed than normal edges, and “peripheral in‐path edges”, which are less traversed than normal edges. Compared with other simple measures such as betweenness centrality, WIPER provides better biological interpretations. In the case study of analyzing postanal pig hearts gene expression, WIPER highlighted new signaling pathways suggestive of cardiomyocyte regeneration and proliferation. In the case study of Alzheimer’s disease genetic disorder association, WIPER reports SRC:APP, AR:APP, APP:FYN, and APP:NES edges (gene‐gene associations) both statistically and biologically important from PubMed co‐citation. Conclusion We believe that WIPER will become an essential software tool to help biologists discover and validate essential signaling/regulatory events from high‐throughput biology data in the context of biological networks. Availability The free WIPER API is described at discovery.informatics.uab.edu/wiper/