Support Vector Machines for Protein Functional Classification
ARMY MEDICAL RESEARCH AND MATERIEL COMMAND FORT DETRICK MD TELEMEDICINE AND ADVANCED TECH RESEARCH CENTER
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We demonstrate that support vector machines svms with selective kernel scaling are an effective tool in discriminating between benign and pathologic proteins. initial results compare favorably against manual classification performed by experts and indicate the capability of svms to capture the underlying structure of the data. the data set consists of 70 proteins of human antibody k1 immunoglobulin light chains, each represented by aligned sequences of 120 amino acids. we perform feature selection based on a first-order adaptive scaling algorithm, which confirms the importance of changes in certain amino acid positions and identifies other positions that are key in the characterization of protein function.