Informative gene selection using the Algebraic Connectivity Strength of Point and Scoring Criteria

This paper presents a novel method that integrates the Algebraic Connectivity Strength of Point (ACSP) and Scoring Criteria to identify genes associated with tumor type. First, for each gene, the ACSP is used to identify reliable expression levels of the gene in all the samples. The informative genes are then selected using Scoring Criteria based on these reliable expression levels. Finally, the Support Vector Machine (SVM) classifier is used to classify the two datasets of gene expression profiling. The results show that the informative genes selected by the proposed method have higher credibility than those selected by Scoring Criteria alone.