TY - JOUR
T1 - Mining Similar Aspects for Gene Similarity Explanation Based on Gene Information Network
AU - Zhang, Yidan
AU - Duan, Lei
AU - Zheng, Huiru
AU - Li-Ling, Jesse
AU - Qin, Ruiqi
AU - Chen, Zihao
AU - He, Chengxin
AU - Wang, Tingting
N1 - Publisher Copyright:
IEEE
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
KW - Biology
KW - Diseases
KW - Ontologies
KW - Peer-to-peer computing
KW - Proteins
KW - Search problems
KW - Semantics
KW - gene information network
KW - gene meta path
KW - similar aspect
UR - https://pure.ulster.ac.uk/en/publications/mining-similar-aspects-for-gene-similarity-explanation-based-on-g
UR - http://www.scopus.com/inward/record.url?scp=85097412663&partnerID=8YFLogxK
U2 - 10.1109/TCBB.2020.3041559
DO - 10.1109/TCBB.2020.3041559
M3 - Article
C2 - 33259307
SP - 1
EP - 13
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
SN - 1545-5963
ER -