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Protein Homology Detection Through Alignment of Markov Random Fields [electronic resource] : Using MRFalign / by Jinbo Xu, Sheng Wang, Jianzhu Ma.

By: Xu, Jinbo [author.].
Contributor(s): Wang, Sheng [author.] | Ma, Jianzhu [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computer Science: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Description: VIII, 51 p. 13 illus., 1 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319149141.Subject(s): Computer science | Mathematical statistics | Bioinformatics | Statistics | Computer Science | Computational Biology/Bioinformatics | Probability and Statistics in Computer Science | Bioinformatics | Statistics for Life Sciences, Medicine, Health SciencesAdditional physical formats: Printed edition:: No titleDDC classification: 570.285 Online resources: Click here to access online
Contents:
Introduction -- Method -- Software -- Experiments and Results -- Conclusion.
In: Springer eBooksSummary: 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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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.

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