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.
|Number of pages||13|
|Journal||IEEE/ACM Transactions on Computational Biology and Bioinformatics|
|Early online date||1 Dec 2020|
|Publication status||E-pub ahead of print - 1 Dec 2020|
Bibliographical notePublisher Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
- Peer-to-peer computing
- Search problems
- gene information network
- gene meta path
- similar aspect