Latent Semantic Analysis- and Hierarchical Clustering-Based Method for Detecting Remote Protein Homology

Title:Latent Semantic Analysis- and Hierarchical Clustering-Based Method for Detecting Remote Protein Homology

Volume: 13 Issue: 2

Author(s): Tianjiao Zhang, Yue Jiang, Liang Cheng, Yang Hu and Yadong Wang

Affiliation:

Keywords: Bioinformatics, Latent Semantic Analysis, Hierarchical Clustering, Remote Protein Homology.

Abstract: Background: The detection of remote homology between protein sequences is a central problem in computational biology. Discriminative methods such as the support vector machine (SVM) are among the most effective approaches.

Objective: Many SVM-based methods focus on finding useful representations of protein sequences using either explicit feature vector representations or kernel functions. Such representations may suffer from the peaking phenomenon in many machine-learning methods because the features are usually very large and may contain some noise. In addition, the dataset for the problem of remote homology detection is imbalanced as the number of negative samples is far greater than the number of positive samples.

Method: Based on these observations, we propose a new method for reconstructing feature space based on latent semantic analysis (LSA) and hierarchical clustering. In addition, for detecting remote homology, we adopt an alternative evaluation method called the precision-recall (PR) curve & score instead of the receiver operating characteristic (ROC).

Results: Compared to existing methods, the performance increased by 14% on the 3-gram features and 7% on the LA features.

Conclusion: Through analysis of the contrasting experiment results, we confirmed that our method is effective and performs better than other existing methods.

Cite this article as:

Zhang Tianjiao, Jiang Yue, Cheng Liang, Hu Yang and Wang Yadong, Latent Semantic Analysis- and Hierarchical Clustering-Based Method for Detecting Remote Protein Homology, Current Proteomics 2016; 13 (2) . https://dx.doi.org/10.2174/157016461302160514003220