Prediction Of Protein Subcellular Localization Using Support Vector Machines

Prediction of Protein Subcellular Localization Using Support Vector Machines

Proceedings of the 6th Asia-Pacific Bioinformatics Conference

High-throughput sequencing and functional genomics technologies have given us the human genome sequence as well as those of other experimentally, medically, and agriculturally important species, thus enabling large-scale genotyping and gene expression profiling of human populations. Databases containing large numbers of sequences, polymorphisms, structures, metabolic pathways, and gene expression profiles of normal and diseased tissues are rapidly being generated for human and model organisms. Bioinformatics is therefore gaining importance in the annotation of genomic sequences; the understanding of the interplay among and between genes and proteins; the analysis of the genetic variability of species; the identification of pharmacological targets; and the inference of evolutionary origins, mechanisms, and relationships. This proceedings volume contains an up-to-date exchange of knowledge, ideas, and solutions to conceptual and practical issues of bioinformatics by researchers, professionals, and industry practitioners at the 6th Asia-Pacific Bioinformatics Conference held in Kyoto, Japan, in January 2008.

Machine Learning for Protein Subcellular Localization Prediction

Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimensionality reduction.