机器学习技术在生物信息挖掘中的方案探讨

Scheme Exploration of Machine learning approaches in Bioinformatics Mining

后基因时代,探索和解释隐藏在分子生物学数据库中的有用信息是对生物信息学研究人员的巨大挑战!为了解决分子生物学中遇到的这些难题,有效及廉价的方法是非常必要的.机器学习是一个崭新的计算机应用领域,而生物信息学是生物学与计算机科学以及应用数学等学科相互交叉而形成的一门新兴学科.本文分析了机器学习技术的内容,介绍了生物信息学的内涵和新的应用技术,同时探索了机器学习技术对生物信息挖掘应用的途径.这些方法有助于加速生物分子结构预测、基因发现、基因组学和蛋白组学等方面的研究进展.

Exploring and explaining the knowledge hidden in the biomolecular has become the grand challenge for bioinformatics in the post genome era. An efficient and inexpensive approach is required to solve problems in molecular biology ;Machine learning is a brand--new computer application realm, but bioinformatics is a newly arising course that biology crosses with computer science and applied mathematics etc. This paper outlines machine learning contents and research methods, introduce the content that bioinformatics with the new applied technique, introduces and scoops out the applied path to bioinformation mining at the same time. These approaches help to accelerate several major researches （biomolecular structure prediction, gene finding, genomics and protemics）.