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Gene expression (micro array) data have been used widely in bioinformatics. The expression data of a large number of genes from small numbers of subjects are used to identify informative biomarkers that may predict or help in diagnosing some disorders. More recently, increasing amounts of information from underlying relationships of the expressed genes have become available, and workers have started...
The subcellular locations of proteins are important functional annotations. An effective and reliable subcellular localization method is necessary for proteomics research. This paper introduces a new method - PairProSVM - to automatically predict the subcellular locations of proteins. The profiles of all protein sequences in the training set are constructed by PSI-BLAST, and the pairwise profile alignment...
Interactions between transcription factors (TFs) are necessary for deciphering the complex mechanisms of transcription regulation in eukaryotes. In this paper, we proposed a novel HV-kernel based Support Vector Machine classifier (HV-SVM) to predict TF-TF interactions based on their protein domain information and GO annotations. Specifically, two types of pairwise kernels, namely, a horizontal kernel...
Microarray data analysis is notoriously challenging as it involves a huge number of genes compared to only a limited number of samples. Gene selection, to detect the most significantly differentially expressed genes under different categories of conditions, is both computationally and biologically interesting, and has become a central research focus in all studies that use gene expression microarray...
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