Using gene ontology to enhance effectiveness of similarity measures for microarray data.
Reducing redundancy is an important goal for most feature selection methods. Almost all methods for redundancy reduction are based on the correlation between gene expression levels. In this paper, we utilise the knowledge in Gene Ontology to provide a new model for measuring redundancy among genes. We propose a novel similarity measure, which incorporates semantic and expression level similarities. We compare our method with traditional expression value-only similarity model on several public microarray datasets. The experimental results show that our approach is capable of offering higher or the same classification accuracy while providing a smaller gene feature.
Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, A1B 3X5, Canada. zchen@mun.ca
Zheng
Z
Chen
Jian
J
Tang
D000465:Algorithms; D016208:Databases, Factual; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D018875:Vocabulary, Controlled