DocumentCode :
975320
Title :
Machine Learning Methods for Protein Structure Prediction
Author :
Cheng, Jianlin ; Tegge, Allison N. ; Baldi, Pierre
Author_Institution :
Comput. Sci. Dept., Univ. of Missouri, Columbia, MO
Volume :
1
fYear :
2008
fDate :
6/30/1905 12:00:00 AM
Firstpage :
41
Lastpage :
49
Abstract :
Machine learning methods are widely used in bioinformatics and computational and systems biology. Here, we review the development of machine learning methods for protein structure prediction, one of the most fundamental problems in structural biology and bioinformatics. Protein structure prediction is such a complex problem that it is often decomposed and attacked at four different levels: 1-D prediction of structural features along the primary sequence of amino acids; 2-D prediction of spatial relationships between amino acids; 3-D prediction of the tertiary structure of a protein; and 4-D prediction of the quaternary structure of a multiprotein complex. A diverse set of both supervised and unsupervised machine learning methods has been applied over the years to tackle these problems and has significantly contributed to advancing the state-of-the-art of protein structure prediction. In this paper, we review the development and application of hidden Markov models, neural networks, support vector machines, Bayesian methods, and clustering methods in 1-D, 2-D, 3-D, and 4-D protein structure predictions.
Keywords :
Bayes methods; bioinformatics; hidden Markov models; learning (artificial intelligence); medical expert systems; molecular biophysics; neural nets; proteins; support vector machines; 3D prediction; 4D quaternary structure prediction; Bayesian methods; amino acids; bioinformatics; clustering methods; computational biology; hidden Markov models; machine learning methods; multiprotein complex; neural networks; protein structure prediction; protein tertiary structure; spatial relationship prediction; structural biology; support vector machines; systems biology; Amino acids; Bioinformatics; Biology computing; Hidden Markov models; Learning systems; Neural networks; Protein engineering; Sequences; Support vector machines; Systems biology; Bioinformatics; machine learning; protein folding; protein structure prediction; Bayes Theorem; Markov Chains; Models, Molecular; Neural Networks (Computer); Protein Conformation; Proteins; Sequence Analysis, Protein;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Reviews in
Publisher :
ieee
ISSN :
1937-3333
Type :
jour
DOI :
10.1109/RBME.2008.2008239
Filename :
4664428
Link To Document :
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