Protein Fold Classification Using Large Margin Combination of Distance Metrics

Chendra Hadi SURYANTO  Kazuhiro FUKUI  Hideitsu HINO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.3   pp.714-723
Publication Date: 2016/03/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDP7294
Type of Manuscript: PAPER
Category: Pattern Recognition
Keyword: 
protein fold classification,  distance metrics combination,  large margin nearest neighbor,  kernel learning,  optimization,  

Full Text: PDF(756.1KB)>>
Buy this Article




Summary: 
Many methods have been proposed for measuring the structural similarity between two protein folds. However, it is difficult to select one best method from them for the classification task, as each method has its own strength and weakness. Intuitively, combining multiple methods is one solution to get the optimal classification results. In this paper, by generalizing the concept of the large margin nearest neighbor (LMNN), a method for combining multiple distance metrics from different types of protein structure comparison methods for protein fold classification task is proposed. While LMNN is limited to Mahalanobis-based distance metric learning from a set of feature vectors of training data, the proposed method learns an optimal combination of metrics from a set of distance metrics by minimizing the distances between intra-class data and enlarging the distances of different classes' data. The main advantage of the proposed method is the capability in finding an optimal weight coefficient for combination of many metrics, possibly including poor metrics, avoiding the difficulties in selecting which metrics to be included for the combination. The effectiveness of the proposed method is demonstrated on classification experiments using two public protein datasets, namely, Ding Dubchak dataset and ENZYMES dataset.