Whole-Genome Functional Classification of Genes by Latent Semantic Analysis on Microarray Data

Quantitative simultaneous monitoring of the expression levels of thousands of genes under various experimental conditions is now possible using microarray experiments. The resulting microarray data are very useful for elucidating the functional relationships among genes in the genomes. However, due to the experimental and biological nature of the data, wholegenome functional classification of genes on microarray data remains a challenging machine learning problem. In this paper, we introduce the application of latent semantic analysis (LSA) to microarray expression data for systematic, genome-wide functional classification of genes. In the LSA approach considered here, singular value decomposition is first applied as a dimensionreducing step on the gene expression data, followed by an unsupervised clustering procedure based on vector similarities in the truncated space. Functional classification is then conducted through calling by majority on each of the resulting gene clusters. Usin...

• Type: Conference
• Year: 2004
• Where: APBC
• Authors: See-Kiong Ng, Zexuan Zhu, Yew-Soon Ong
• Keywords: APBC 2004, APBC 2007, Expression Data, Functional Classification, Microarray Data