SCENARIO: Discovery of Similar Aspects for Gene Similarity Explanation from Gene Information Network

Yidan Zhang, Lei Duan, Huiru Zheng, Jesse Li-Ling, Bin Hu, Ruiqi Qin, Chengxin He

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Gene similarity analysis not only provides information on understanding the biological roles and functions of a gene, but also reveals the relationships among different genes. In this paper, we identify the novel idea of mining similar aspects from gene information network, i.e., given a pair of genes, we want to know, in which aspects (meta paths) the two genes are mostly similar from the perspective of gene information network? We define a similarity metric based on the set of meta paths connecting the query genes in the gene information network, and use the rank of the similarity of a gene pair in a meta path set to measure the similarity significance in the aspect. A minimal set of meta paths where the query gene pair is ranked the best is a similar aspect. Computing the similar aspects of a query gene pair is far from trivial. In this paper, we propose a novel heuristic based-mining method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from seven public gene-related databases, verified that our proposed method is effective, efficient, and useful.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Bioinformatics and Biomedicine(BIBM 2019)
Place of PublicationSan Diego, CA, USA
Pages604-609
Number of pages5
ISBN (Electronic)978-1-7281-1867-3
DOIs
Publication statusPublished - 1 Dec 2019

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