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

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.