Similar genes discovery system (SGDS): Application for predicting possible pathways by using GO semantic similarity measure

Jung Hsien Chiang, Shing Hua Ho, Wen Hung Wang

研究成果: Article同行評審

6 引文 斯高帕斯(Scopus)

摘要

This research analyzes the gene relationship according to their annotations. We present here a similar genes discovery system (SGDS), based upon semantic similarity measure of gene ontology (GO) and Entrez gene, to identify groups of similar genes. In order to validate the proposed measure, we analyze the relationships between similarity and expression correlation of pairs of genes. We explore a number of semantic similarity measures and compute the Pearson correlation coefficient. Highly correlated genes exhibit strong similarity in the ontology taxonomies. The results show that our proposed semantic similarity measure outperforms the others and seems better suited for use in GO. We use MAPK homogenous genes group and MAP kinase pathway as benchmarks to tune the parameters in our system for achieving higher accuracy. We applied the SGDS to RON and Lutheran pathways, the results show that it is able to identify a group of similar genes and to predict novel pathways based on a group of candidate genes.

原文English
頁(從 - 到)1115-1121
頁數7
期刊Expert Systems With Applications
35
發行號3
DOIs
出版狀態Published - 2008 十月 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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