Protein Homology Detection Through Alignment of Markov Random Fields Using MRFalign /

• Κύριοι συγγραφείς: Xu, Jinbo (Συγγραφέας), Wang, Sheng (Συγγραφέας), Ma, Jianzhu (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2015.
• Σειρά: SpringerBriefs in Computer Science,
• Θέματα: Computer science.; Mathematical statistics.; Bioinformatics.; Statistics.; Computer Science.; Computational Biology/Bioinformatics.; Probability and Statistics in Computer Science.; Statistics for Life Sciences, Medicine, Health Sciences.
• Φυσική περιγραφή: VIII, 51 p. 13 illus., 1 illus. in color. online resource.
• ISBN: 9783319149141
• ISSN: 2191-5768

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.