title: Prediction of protein secondary structure using Large Margin Nearest Neighbour classification.
abstract: In this paper, we introduce a novel method for protein secondary structure prediction by using Position-Specific Scoring Matrices (PSSM) profiles and Large Margin Nearest Neighbour (LMNN) classification. Since the PSSM profiles are not specifically designed for protein secondary structure prediction, the traditional nearest neighbour method could not achieve satisfactory prediction accuracy. To address this problem, we first use a LMNN model to learn a Mahalanobis distance metric for nearest neighbour classification. Then, an energy-based rule is invoked to assign secondary structure. Tests show that the proposed method obtains better prediction accuracy when compared with previous nearest neighbour methods.
authors: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors: Wei
authors: W
authors: Yang
authors: Kuanquan
authors: K
authors: Wang
authors: Wangmeng
authors: W
authors: Zuo
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D056510:Position-Specific Scoring Matrices; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D016415:Sequence Alignment
chemical_list: D011506:Proteins