Mining Similar Aspects for Gene Similarity Explanation Based on Gene Information Network

Yidan Zhang, Lei Duan, Huiru Zheng, Jesse Li-Ling, Ruiqi Qin, Zihao Chen, Chengxin He, Tingting Wang

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Abstract

Analysis of gene similarity not only can provide information on the understanding of the biological roles and functions of a gene, but may also reveal the relationships among various genes. In this paper, we introduce a novel idea of mining similar aspects from a gene information network, i.e., for a given gene pair, we want to know in which aspects (meta paths) they are most similar from the perspective of the gene information network. We defined a similarity metric based on the set of meta paths connecting the query genes in the gene information network and used the rank of similarity of a gene pair in a meta path set to measure the similarity significance in that aspect. A minimal set of gene meta paths where the query gene pair ranks the highest is a similar aspect, and the similar aspect of a query gene pair is far from trivial. We proposed a novel method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from six public gene-related databases, verified that our proposed method is effective, efficient, and useful.
Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Early online date1 Dec 2020
DOIs
Publication statusE-pub ahead of print - 1 Dec 2020

Keywords

  • Biology
  • Diseases
  • Ontologies
  • Peer-to-peer computing
  • Proteins
  • Search problems
  • Semantics
  • gene information network
  • gene meta path
  • similar aspect

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