Overview
- Surveys the key topics to aid the reader in quickly learning about this area
- Presents a novel technique for protein homology search, preparing the reader for future developments
- Provides an introduction to the software and the web server, enabling the reader to easily make use of the new technique
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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About this book
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.
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Keywords
Table of contents (4 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
Bibliographic Information
Book Title: Protein Homology Detection Through Alignment of Markov Random Fields
Book Subtitle: Using MRFalign
Authors: Jinbo Xu, Sheng Wang, Jianzhu Ma
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-14914-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2015
Softcover ISBN: 978-3-319-14913-4Published: 03 March 2015
eBook ISBN: 978-3-319-14914-1Published: 22 January 2015
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: VIII, 51
Number of Illustrations: 12 b/w illustrations, 1 illustrations in colour
Topics: Computational Biology/Bioinformatics, Probability and Statistics in Computer Science, Bioinformatics, Statistics for Life Sciences, Medicine, Health Sciences