用于二级和超二级蛋白质结构预测的机器学习方法综述。

A survey of machine learning methods for secondary and supersecondary protein structure prediction.

作者信息

Ho Hui Kian, Zhang Lei, Ramamohanarao Kotagiri, Martin Shawn

机构信息

Department of Computer Science and Software Engineering, University of Melbourne, National ICT Australia, Parkville, VIC, Australia.

出版信息

Methods Mol Biol. 2013;932:87-106. doi: 10.1007/978-1-62703-065-6_6.

DOI:10.1007/978-1-62703-065-6_6

PMID:22987348

Abstract

In this chapter we provide a survey of protein secondary and supersecondary structure prediction using methods from machine learning. Our focus is on machine learning methods applicable to β-hairpin and β-sheet prediction, but we also discuss methods for more general supersecondary structure prediction. We provide background on the secondary and supersecondary structures that we discuss, the features used to describe them, and the basic theory behind the machine learning methods used. We survey the machine learning methods available for secondary and supersecondary structure prediction and compare them where possible.

摘要

在本章中，我们将概述使用机器学习方法进行蛋白质二级和超二级结构预测的情况。我们重点关注适用于β-发夹和β-折叠预测的机器学习方法，但也会讨论更通用的超二级结构预测方法。我们提供了我们所讨论的二级和超二级结构的背景知识、用于描述它们的特征以及所使用的机器学习方法背后的基本理论。我们概述了可用于二级和超二级结构预测的机器学习方法，并在可能的情况下对它们进行比较。