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Protein Homology Detection Through Alignment of Markov Random Fields - Using MRFalign (Paperback, 2015 ed.) Loot Price: R1,705
Discovery Miles 17 050
Protein Homology Detection Through Alignment of Markov Random Fields - Using MRFalign (Paperback, 2015 ed.): Jinbo Xu, Sheng...

Protein Homology Detection Through Alignment of Markov Random Fields - Using MRFalign (Paperback, 2015 ed.)

Jinbo Xu, Sheng Wang, Jianzhu Ma

Series: SpringerBriefs in Computer Science

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Loot Price R1,705 Discovery Miles 17 050 | Repayment Terms: R160 pm x 12*

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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.

General

Imprint: Springer International Publishing AG
Country of origin: Switzerland
Series: SpringerBriefs in Computer Science
Release date: 2015
First published: 2014
Authors: Jinbo Xu • Sheng Wang • Jianzhu Ma
Dimensions: 235 x 155 x 3mm (L x W x T)
Format: Paperback
Pages: 51
Edition: 2015 ed.
ISBN-13: 978-3-319-14913-4
Categories: Books > Science & Mathematics > Mathematics > Probability & statistics
Books > Science & Mathematics > Biology, life sciences > Molecular biology
Books > Computing & IT > General theory of computing > Mathematical theory of computation
Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers
Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > General
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LSN: 3-319-14913-X
Barcode: 9783319149134

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