PUBLICATION DATE

2008

TITLE

Machine learning methods for protein structure prediction.

ISSUE

N/A

VOLUME

1

ISSN (print)

N/A

ISSN (electronic)

N/A

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.

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    12 TRIPLES      12 PREDICATES      13 URIs      8 LITERALS

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    1 articles:f7774b7d6362e3af2add93280a938489 sg: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.
    2 sg:doi 10.1109/rbme.2008.2008239
    3 sg:doiLink http://dx.doi.org/10.1109/rbme.2008.2008239
    4 sg:isFundedPublicationOf grants:d484c2dcde82cb96530f5bca02d4ea36
    5 sg:language English
    6 sg:license http://scigraph.springernature.com/explorer/license/
    7 sg:publicationYear 2008
    8 sg:scigraphId f7774b7d6362e3af2add93280a938489
    9 sg:title Machine learning methods for protein structure prediction.
    10 sg:volume 1
    11 rdf:type sg:Article
    12 rdfs:label Article: Machine learning methods for protein structure prediction.
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