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Protein homology detection through a...

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Protein homology detection through alignment of Markov random fields/ by Jinbo Xu, Sheng Wang, Jianzhu Ma.
其他題名:	using MRFalign /
作者:	Xu, Jinbo.
其他作者:	Wang, Sheng.
出版者:	Cham :Springer International Publishing : : 2015.,
面頁冊數:	viii, 51 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Markov random fields. -
電子資源:	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:

897937
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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505 0 $a Introduction -- Method -- Software -- Experiments and Results -- Conclusion.
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