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Protein Homology Detection Through Alignment of Markov Random Fields Using MRFalign /
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) b...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
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| Edición: | 1st ed. 2015. |
| Colección: | SpringerBriefs in Computer Science,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-319-14914-1 | ||
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| 005 | 20220118230431.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 150122s2015 sz | s |||| 0|eng d | ||
| 020 | |a 9783319149141 |9 978-3-319-14914-1 | ||
| 024 | 7 | |a 10.1007/978-3-319-14914-1 |2 doi | |
| 050 | 4 | |a QH324.2-324.25 | |
| 072 | 7 | |a PS |2 bicssc | |
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| 082 | 0 | 4 | |a 570.285 |2 23 |
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| 100 | 1 | |a Xu, Jinbo. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Protein Homology Detection Through Alignment of Markov Random Fields |h [electronic resource] : |b Using MRFalign / |c by Jinbo Xu, Sheng Wang, Jianzhu Ma. |
| 250 | |a 1st ed. 2015. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2015. | |
| 300 | |a VIII, 51 p. 13 illus., 1 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a SpringerBriefs in Computer Science, |x 2191-5776 | |
| 505 | 0 | |a Introduction -- Method -- Software -- Experiments and Results -- Conclusion. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Bioinformatics. | |
| 650 | 0 | |a Computer science-Mathematics. | |
| 650 | 0 | |a Mathematical statistics. | |
| 650 | 0 | |a Biometry. | |
| 650 | 1 | 4 | |a Computational and Systems Biology. |
| 650 | 2 | 4 | |a Probability and Statistics in Computer Science. |
| 650 | 2 | 4 | |a Bioinformatics. |
| 650 | 2 | 4 | |a Biostatistics. |
| 700 | 1 | |a Wang, Sheng. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Ma, Jianzhu. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319149134 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319149158 |
| 830 | 0 | |a SpringerBriefs in Computer Science, |x 2191-5776 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-14914-1 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||