Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Prediction of Protein Secondary Structure with two-stage multi-class SVMs
International Journal of Data Mining and Bioinformatics
Improving protein secondary structure predictions by prediction fusion
Information Fusion
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
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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.