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Protein homology detection through a...

Xu, Jinbo.

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Protein homology detection through alignment of Markov random fields = using MRFalign /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Protein homology detection through alignment of Markov random fields/ by Jinbo Xu, Sheng Wang, Jianzhu Ma.
Reminder of title:	using MRFalign /
Author:	Xu, Jinbo.
other author:	Wang, Sheng.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	viii, 51 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Method -- Software -- Experiments and Results -- Conclusion.
Contained By:	Springer eBooks
Subject:	Markov random fields. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-14914-1
ISBN:	9783319149141 (electronic bk.)

Protein homology detection through alignment of Markov random fields = using MRFalign /
Xu, Jinbo.

Protein homology detection through alignment of Markov random fieldsusing MRFalign /[electronic resource] :by Jinbo Xu, Sheng Wang, Jianzhu Ma. - Cham :Springer International Publishing :2015. - viii, 51 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

Introduction -- Method -- Software -- Experiments and Results -- Conclusion.

This work covers sequence-based protein homology detection, a fundamental and challenging bioinformatics problem with a variety of real-world applications. The text first surveys a few popular homology detection methods, such as Position-Specific Scoring Matrix (PSSM) and Hidden Markov Model (HMM) based methods, and then describes a novel Markov Random Fields (MRF) based method developed by the authors. MRF-based methods are much more sensitive than HMM- and PSSM-based methods for remote homolog detection and fold recognition, as MRFs can model long-range residue-residue interaction. The text also describes the installation, usage and result interpretation of programs implementing the MRF-based method.

ISBN: 9783319149141 (electronic bk.)

Standard No.: 10.1007/978-3-319-14914-1doiSubjects--Topical Terms:

728525
Markov random fields.

LC Class. No.: QA274.45

Dewey Class. No.: 519.23

Protein homology detection through alignment of Markov random fields = using MRFalign /
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245 1 0 $a Protein homology detection through alignment of Markov random fields $h [electronic resource] : $b using MRFalign / $c by Jinbo Xu, Sheng Wang, Jianzhu Ma.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a viii, 51 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5768
505 0 $a Introduction -- Method -- Software -- Experiments and Results -- Conclusion.
520 $a This work covers sequence-based protein homology detection, a fundamental and challenging bioinformatics problem with a variety of real-world applications. The text first surveys a few popular homology detection methods, such as Position-Specific Scoring Matrix (PSSM) and Hidden Markov Model (HMM) based methods, and then describes a novel Markov Random Fields (MRF) based method developed by the authors. MRF-based methods are much more sensitive than HMM- and PSSM-based methods for remote homolog detection and fold recognition, as MRFs can model long-range residue-residue interaction. The text also describes the installation, usage and result interpretation of programs implementing the MRF-based method.
650 0 $a Markov random fields. $3 728525
650 0 $a Sequence alignment (Bioinformatics) $3 2062400
650 0 $a Bioinformatics. $3 553671
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