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

• Autore: Hŏ Kŭm-p'a
• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
• Edizione: 1st ed. 2015.
• Descrizione fisica: 1 online resource (59 p.)
• Disciplina: 004; 005.55; 519.5; 570285
• Soggetto topico: Bioinformatics; Mathematical statistics; Statistics; Computational Biology/Bioinformatics; Probability and Statistics in Computer Science; Statistics for Life Sciences, Medicine, Health Sciences
• Persona (resp. second.): WangSheng; MaJianzhu
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Introduction -- Method -- Software -- Experiments and Results -- Conclusion.
• Sommario/riassunto: 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.
• Titolo autorizzato: Protein Homology Detection Through Alignment of Markov Random Fields
• ISBN: 3-319-14914-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910299230003321
• Lo trovi qui: Univ. Federico II
• Serie: SpringerBriefs in Computer Science, . 2191-5768