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Record Nr. |
UNINA9910299230003321 |
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Autore |
Xu Jinbo |
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Titolo |
Protein Homology Detection Through Alignment of Markov Random Fields : Using MRFalign / / by Jinbo Xu, Sheng Wang, Jianzhu Ma |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
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ISBN |
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Edizione |
[1st ed. 2015.] |
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Descrizione fisica |
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1 online resource (59 p.) |
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Collana |
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SpringerBriefs in Computer Science, , 2191-5768 |
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Disciplina |
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Soggetti |
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Bioinformatics |
Mathematical statistics |
Statistics |
Computational Biology/Bioinformatics |
Probability and Statistics in Computer Science |
Statistics for Life Sciences, Medicine, Health Sciences |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Introduction -- Method -- Software -- Experiments and Results -- Conclusion. |
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Sommario/riassunto |
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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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