Xu, Jinbo.
Protein Homology Detection Through Alignment of Markov Random Fields Using MRFalign / [electronic resource] : by Jinbo Xu, Sheng Wang, Jianzhu Ma. - VIII, 51 p. 13 illus., 1 illus. in color. online resource. - 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.
9783319149141
10.1007/978-3-319-14914-1 doi
Computer science.
Mathematical statistics.
Bioinformatics.
Statistics.
Computer Science.
Computational Biology/Bioinformatics.
Probability and Statistics in Computer Science.
Bioinformatics.
Statistics for Life Sciences, Medicine, Health Sciences.
QH324.2-324.25
570.285
Protein Homology Detection Through Alignment of Markov Random Fields Using MRFalign / [electronic resource] : by Jinbo Xu, Sheng Wang, Jianzhu Ma. - VIII, 51 p. 13 illus., 1 illus. in color. online resource. - 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.
9783319149141
10.1007/978-3-319-14914-1 doi
Computer science.
Mathematical statistics.
Bioinformatics.
Statistics.
Computer Science.
Computational Biology/Bioinformatics.
Probability and Statistics in Computer Science.
Bioinformatics.
Statistics for Life Sciences, Medicine, Health Sciences.
QH324.2-324.25
570.285