Knowledge based-clustering and application of multi-class SVM for genes expression analysis

This study utilizes Support Vector Machines (SVM) for multi-class classification of a set of E. coli whole-genome gene expression profiles. The problem is how to classify these genes based on their behavior in response to changing pH of the growth medium and mutation of the acid tolerance response gene regulator GadX. K-Means clustering is applied in a multi-level scheme to label the genes. Multi-level K-Means is itself an improvement over standard K-Means applications. The labels indicate the response of genes to the experimental variables: 1-unchanged, 2-decreased expression level and 3-increased expression level. Then, SVM is used to confirm the labels resulting from multi-level K-Means. Multi-class SVM with one-against-one method and one-against-all method is used. To judge the performance, Learning Vector Quantization (LVQ) and Linear Discriminant Analysis (LDA) are implemented. The results show that SVM outperforms LVQ and LDA. The advantage of SVM includes the generalization error and the computing time.