生物信息智能化处理的进展

PROGRESS IN ARTIFICIAL INTELLIGENCE TECHNIQUES FOR BIOINFORMATICS

回顾了人工智能在生物信息处理中的进展,重点介绍了神经网络、符号机器学习和遗传算法在处理生物信息中的应用情况.每种技术都列举了相应的例子,包括了蛋白质折叠预测、痫毒蛋白酶分裂预测、分类、序列联配、微集阵列基因表达分析等.同时也介绍了其它一些人工智能技术在生物信息学中的应用动向.

The article aims to provide an overview of the ways in which techniques from artificial intelligence can be usefully employed in bioinformatics. That covers three techniques:symbolic machine learning approaches(nearest-neighbour and identification tree techniques), artificial neural networks and genetic algorithms. Each technique is introduced and then supported with examples taken from the bioinformati(?)s literature. These examples include folding prediction, viral protease cleavage prediction, classification, multiple sequence alignment and microarray gene expression analysis. In addition, the other techniques for bioinformati(?)s such as Neural Tree, BBN, HMM, ILP, and SVM are introduced.