Gene selection algorithms for microarray data based on least squares support vector machine

• Published in: BMC bioinformatics Vol. 7; no. 1; p. 95
• Main Authors: Tang, E Ke, Suganthan, PN, Yao, Xin
• Format: Journal Article
• Language: English
• Published: England BioMed Central 27.02.2006; BMC
• Subjects: Algorithms; Artificial Intelligence; Gene Expression Profiling - methods; Least-Squares Analysis; Oligonucleotide Array Sequence Analysis - methods; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-7-95

In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary to conduct the discriminant analysis with all the genes. Hence, gene selection is usually performed to select important genes. A gene selection method searches for an optimal or near optimal subset of genes with respect to a given evaluation criterion. In this paper, we propose a new evaluation criterion, named the leave-one-out calculation (LOOC, A list of abbreviations appears just above the list of references) measure. A gene selection method, named leave-one-out calculation sequential forward selection (LOOCSFS) algorithm, is then presented by combining the LOOC measure with the sequential forward selection scheme. Further, a novel gene selection algorithm, the gradient-based leave-one-out gene selection (GLGS) algorithm, is also proposed. Both of the gene selection algorithms originate from an efficient and exact calculation of the leave-one-out cross-validation error of the least squares support vector machine (LS-SVM). The proposed approaches are applied to two microarray datasets and compared to other well-known gene selection methods using codes available from the second author. The proposed gene selection approaches can provide gene subsets leading to more accurate classification results, while their computational complexity is comparable to the existing methods. The GLGS algorithm can also better scale to datasets with a very large number of genes.