Application of latent semantic analysis to protein remote homology detection
Application of latent semantic analysis to protein remote homology detection
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DOI：
10.1093/bioinformatics/bti801
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发表时间：
2006-02-01
期刊：
影响因子：
5.8
通讯作者：
Lin, L
中科院分区：
文献类型：
--
作者：
Dong, QW;Wang, XL;Lin, L
动机：蛋白质序列之间的远程同源性检测是计算生物学中的一个核心问题。支持向量机（SVM）等判别方法是最有效的方法之一。许多基于支持向量机的方法集中在寻找有用的蛋白质序列表示，使用显式特征向量表示或核函数。在许多机器学习方法中，这样的表示可能遭受峰值现象，因为特征通常非常大并且可能引入噪声数据。基于这些观察，本研究的重点是特征提取和有效的表示SVM蛋白质classification.Results：在这项研究中，潜在语义分析（LSA）模型，这是一个有效的特征提取技术，从自然语言处理，已被引入蛋白质远程同源检测。蛋白质序列的几个基本组成部分已经被研究为“蛋白质序列语言”的“词”，包括N元、模式和基序。每个蛋白质序列被视为一个由词袋组成的“文档”。首先构造词-文档矩阵。对该矩阵进行LSA，生成蛋白质序列的潜在语义表示向量，从而实现了对蛋白质序列的去噪和智能描述。潜在语义表示向量，然后通过SVM进行评估。该方法在SCOP 1.53数据库上进行了测试。结果表明，LSA模型显着提高了远程同源检测的性能相比，基本的形式主义。此外，该方法的性能与复杂的核方法，如SVM-LA，优于其他基于序列的方法，如PSI-BLAST和SVM-pairwise。
Motivation: Remote homology detection between protein sequences is a central problem in computational biology. The discriminative method such as the support vector machine (SVM) is one of the most effective methods. Many of the SVM-based methods focus on finding useful representations of protein sequence, 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 noise data may be introduced. Based on these observations, this research focuses on feature extraction and efficient representation of protein vectors for SVM protein classification.Results: In this study, a latent semantic analysis (LSA) model, which is an efficient feature extraction technique from natural language processing, has been introduced in protein remote homology detection. Several basic building blocks of protein sequences have been investigated as the 'words' of 'protein sequence language', including N-grams, patterns and motifs. Each protein sequence is taken as a 'document' that is composed of bags-of-word. The word-document matrix is constructed first. The LSA is performed on the matrix to produce the latent semantic representation vectors of protein sequences, leading to noise-removal and smart description of protein sequences. The latent semantic representation vectors are then evaluated by SVM. The method is tested on the SCOP 1.53 database. The results show that the LSA model significantly improves the performance of remote homology detection in comparison with the basic formalisms. Furthermore, the performance of this method is comparable with that of the complex kernel methods such as SVM-LA and better than that of other sequence-based methods such as PSI-BLAST and SVM-pairwise.