WIPER: Weighted in-Path Edge Ranking for biomolecular association networks

doi:10.1007/s40484-019-0180-y

Quantitative Biology

2019, Vol. 7

Issue (4): 313-326 https://doi.org/10.1007/s40484-019-0180-y

本期目录

WIPER: Weighted in-Path Edge Ranking for biomolecular association networks

Zongliang Yue¹, Thanh Nguyen¹, Eric Zhang², Jianyi Zhang², Jake Y. Chen^1,^2,³(

)

¹. Informatics Institute, School of Medicine, University of Alabama, Birmingham, AL 35233, USA
². Department of Biomedical Engineering, University of Alabama, Birmingham, AL 35233, USA
³. Department of Computer Science, University of Alabama, Birmingham, AL 35233, USA

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Abstract：

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/

收稿日期: 2019-06-01 出版日期: 2019-12-31

Corresponding Author(s): Jake Y. Chen

引用本文:

. [J]. Quantitative Biology, 2019, 7(4): 313-326.
Zongliang Yue, Thanh Nguyen, Eric Zhang, Jianyi Zhang, Jake Y. Chen. WIPER: Weighted in-Path Edge Ranking for biomolecular association networks. Quant. Biol., 2019, 7(4): 313-326.

链接本文:

https://academic.hep.com.cn/qb/CN/10.1007/s40484-019-0180-y
https://academic.hep.com.cn/qb/CN/Y2019/V7/I4/313

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