Protein Homology Detection Through Alignment of Markov Random Fields : 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:
TextLanguage: İngilizce Series: SpringerBriefs in Computer SciencePublisher: Cham : Springer International Publishing : Imprint: Springer, 2015Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319149141Subject(s): Bioinformatics | Computer science | Statistics | Computational Biology/Bioinformatics | Probability and Statistics in Computer Science | Bioinformatics | Statistics for Life Sciences, Medicine, Health SciencesLOC classification: QH324.2-324.25Online resources: Springer eBooks Online access link to the resource | Item type | Current location | Home library | Collection | Call number | Status | Notes | Date due | Barcode |
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E-Book
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Merkez Kütüphane | Merkez Kütüphane | E-Kitap Koleksiyonu | QH324.2-324.25EBK (Browse shelf) | Geçerli değil-e-Kitap / Not applicable-e-Book | BİL | EBK00783 |
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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