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首页> 外文期刊>International Journal of Pharmaceutical Sciences and Research >MULTI DIMENSION PROTEIN IMPACT MATRIX BASED PROTEIN SEQUENCE PREDICTION USING DATA MINING
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MULTI DIMENSION PROTEIN IMPACT MATRIX BASED PROTEIN SEQUENCE PREDICTION USING DATA MINING

机译:使用数据挖掘的多维蛋白质碰撞矩阵基于蛋白质序列预测

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摘要

Proteins are the most essential and versatile macromolecules of life, and the knowledge of their functions is a crucial link in the development of new drugs, better crops, and even the development of synthetic biochemicals such as biofuels. Experimental procedures for protein function prediction are inherently low throughput and are thus unable to annotate a non-trivial fraction of proteins that are becoming available due to rapid advances in genome sequencing technology. This has motivated the development of computational techniques that utilize a variety of high-throughput experimental data for protein function prediction, such as protein and genome sequences, gene expression data, protein interaction networks and phylogenetic profiles.
机译:蛋白质是生命中最重要,用途最广泛的大分子,其功能知识是开发新药,改良农作物甚至开发合成生物化学物质(如生物燃料)的关键环节。用于蛋白质功能预测的实验程序本质上是低通量的,因此无法注释由于基因组测序技术的快速发展而变得可用的蛋白质的重要​​部分。这激发了利用多种高通量实验数据进行蛋白质功能预测的计算技术的发展,例如蛋白质和基因组序列,基因表达数据,蛋白质相互作用网络和系统发育谱。

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