Abstract
Identifying functionally similar or closely related genes and gene products has significant impacts on biological and clinical studies as well as drug discovery. In this paper, we propose an effective and practically useful method measuring both gene and gene product similarity by integrating the topology of gene ontology, known functional domains and their functional annotations. Theproposed method is comprehensively evaluated through statistical analysis of the similarities derived from sequence, structure and phylogenetic profiles, and clustering analysis of disease genes clusters. Our results show that the proposed method clearlyoutperforms other conventional methods. Furthermore, literature analysis also reveals that the proposed method is both statistically and biologically promising for identifying functionally similar genes or gene products. In particular, we demonstrate that the proposed functional similarity metric is capable of discoverying new disease related genes or gene products.
Original language | English |
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Article number | 6872589 |
Pages (from-to) | 322-334 |
Number of pages | 13 |
Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Volume | 12 |
Issue number | 2 |
DOIs | |
State | Published - Mar 1 2015 |
Bibliographical note
Publisher Copyright:© 2004-2012 IEEE.
Keywords
- Bioinformatics (genome and proteome) databases
- Biology and genetics
- Clustering
- Similarity measures
ASJC Scopus subject areas
- Biotechnology
- Genetics
- Applied Mathematics