Novel PSSM-Based Approaches for Gene Identification Using Support Vector Machine

Heena Farooq Bhat, M. Wani
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引用次数: 2

Abstract

By understanding the function of each protein encoded in genome, the molecular mechanism of the cell can be recognized. In genome annotation field, several methods or techniques have been developed to locate or predict the patterns of genes in genome sequence. However, recognizing corresponding gene of a given protein sequence using conventional tools is inherently complicated and error prone. This paper first focuses on the issue of gene prediction and its challenges. The authors then present a novel method for identifying genes that involves a two-step process. First the research presents new features extracted from protein sequences using a position specific scoring matrix (PSSM). The PSSM profiles are converted into uniform numeric representation. Then, a new structured approach has been applied on PSSM vector which uses a decision tree-based technique for obtaining rules. Finally, the rules of single class are joined together to form a matrix which is then given as an input to SVM for classification purpose. The rules derived from algorithm correspond to genes. The authors also introduce another approach for predicting genes based on PSSM using SVM. Both the methods have been implemented on genome DNAset dataset. Empirical evaluation shows that PSSM based SAFARI approach produces better results.
基于pssm的支持向量机基因识别新方法
通过了解基因组中编码蛋白的功能,可以认识细胞的分子机制。在基因组注释领域,已经发展了几种定位或预测基因组序列中基因模式的方法或技术。然而,使用传统工具识别给定蛋白质序列的相应基因本身就很复杂且容易出错。本文首先介绍了基因预测的问题及其面临的挑战。作者随后提出了一种识别基因的新方法,该方法涉及两步过程。首先,该研究提出了使用位置特定评分矩阵(PSSM)从蛋白质序列中提取新的特征。将PSSM配置文件转换为统一的数字表示。然后,在PSSM向量上应用了一种新的结构化方法,该方法使用基于决策树的技术来获取规则。最后,将单个类的规则连接在一起形成一个矩阵,然后将该矩阵作为支持向量机的输入进行分类。算法产生的规则对应于基因。作者还介绍了另一种基于支持向量机的PSSM基因预测方法。这两种方法都在基因组dna数据库上实现了。实证评价表明,基于PSSM的SAFARI方法具有较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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