Gene classification artificial neural system

Cathy H. Wu, Hsi-Lien Chen, Sheng-Chih Chen
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引用次数: 32

Abstract

A gene classification artificial neural system has been developed for rapid annotation of the molecular sequencing data being generated by the Human Genome Project. Three neural networks have been implemented, one full-scale system to classify protein sequences according to PIR (protein identification resources) superfamilies, one system to classify ribosomal RNA sequences into RDP (ribosomal database project) phylogenetic classes, and one pilot system to classify proteins according to Blocks motifs. The sequence encoding schema involved an n-gram hashing method to convert molecular sequences into neural input vectors, a SVD (singular value decomposition) method to compress vectors, and a term weighting method to extract motif information. The neural networks used were three-layered, feed-forward networks that employed backpropagation or counter-propagation learning paradigms. The system runs faster by one to two orders of magnitude than existing method and has a sensitivity of 85 to 100%. The gene classification artificial neural system is available on the Internet, and may be extended into a gene identification system for classifying indiscriminately sequenced DNA fragments.<>
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基因分类人工神经系统
一个基因分类人工神经系统被开发用于快速标注由人类基因组计划产生的分子测序数据。目前已经实现了三个神经网络,一个全规模的系统根据PIR(蛋白质鉴定资源)超家族对蛋白质序列进行分类,一个系统将核糖体RNA序列分类为RDP(核糖体数据库项目)系统发育类,一个试点系统根据Blocks基序对蛋白质进行分类。序列编码模式采用n-gram哈希法将分子序列转换为神经输入向量,采用奇异值分解(SVD)法压缩向量,采用项加权法提取基序信息。使用的神经网络是三层前馈网络,采用反向传播或反传播学习范式。该系统的运行速度比现有方法快一到两个数量级,灵敏度为85%至100%。基因分类人工神经系统可在互联网上获得,并可扩展为一个基因识别系统,用于对无差别测序的DNA片段进行分类
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