Hidden Markov Models : methods and protocols / edited by David R. Westhead, M.S. Vijayabaskar
| Medienart: |
Buch |
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| Erscheinungsjahr: |
[2017] © 2017 |
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| Erschienen: |
New York, NY: Humana Press ; 2017 © 2017 |
| Ausgabe: |
1st ed. 2017 |
| Reihe: |
Methods in molecular biology - 1552 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Westhead, David R. [HerausgeberIn] |
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| Links: |
Inhaltstext |
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| ISBN: |
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| Nlm: |
W1 QA 274.7 |
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| Themen: |
Bioinformatics |
| Anmerkungen: |
Includes bibliographical references and index |
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| Umfang: |
x, 221 Seiten ; Illustrationen, Diagramme ; 25.4 cm x 17.8 cm, 0 g |
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| Weitere IDs: |
9781493967513 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
88204186X |
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| 245 | 1 | 0 | |a Hidden Markov Models |b methods and protocols |c edited by David R. Westhead, M.S. Vijayabaskar |
| 250 | |a 1st ed. 2017 | ||
| 264 | 1 | |a New York, NY |b Humana Press |c [2017] | |
| 264 | 4 | |c © 2017 | |
| 300 | |a x, 221 Seiten |b Illustrationen, Diagramme |c 25.4 cm x 17.8 cm, 0 g | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
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| 490 | 1 | |a Methods in molecular biology |v 1552 | |
| 490 | 0 | |a Springer protocols | |
| 500 | |a Includes bibliographical references and index | ||
| 505 | 8 | 0 | |t Introduction to hidden markov models and its applications in biology |r M.S. Vijayabaskar |
| 505 | 8 | 0 | |t Introduction to hidden markov models and its applications in biology |r M.S. Vijayabaskar |
| 505 | 8 | 0 | |t HMMs in protein fold classification |r Christos Lampros, Costas Papaloukas, Themis Exarchos, and Dimitrios I. Fotiadis |
| 505 | 8 | 0 | |t Application of hidden markov models in biomolecular simulations |r Saurabh Shukla, Zahra Shamsi, Alexander S. Moffett, Balaji Selvam, and Diwakar Shukla |
| 505 | 8 | 0 | |t Predicting beta barrel transmembrane proteins using HMMs |r Georgios N. Tsaousis, Stavros J. Hamodrakas, and Pantelis G. Bagos |
| 505 | 8 | 0 | |t Predicting Alpha Helical Transmembrane Proteins Using HMMs |r Georgios N. Tsaousis, Margarita C. Theodoropoulou, Stavros J. Hamodrakas, and Pantelis G. Bagos |
| 505 | 8 | 0 | |t Self-organizing hidden markov model map (SOHMMM) : biological sequence clustering and cluster visualization |r Christos Ferles, William-Scott Beaufort, and Vanessa Ferle |
| 505 | 8 | 0 | |t Analyzing single molecule FRET trajectories using HMM |r Kenji Okamoto |
| 505 | 8 | 0 | |t Modelling ChIP-seq data using HMMs |r Veronica Vinciotti |
| 505 | 8 | 0 | |t Hidden markov models in bioinformatics : SNV inference from next generation sequence |r Jiawen Bian and Xiaobo Zhou |
| 505 | 8 | 0 | |t Computationally tractable multivariate HMM in genome-wide mapping studies |r Hyungwon Choi, Debashis Ghosh, and Zhaohui Qin |
| 505 | 8 | 0 | |t Hidden markov models in population genomics |r Julien Y. Dutheil |
| 505 | 8 | 0 | |t Differential gene expression (DEX) and alternative splicing events (ASE) for temporal dynamic processes using HMMs and hierarchical bayesian modeling approaches |r Sunghee Oh and Seongho Song |
| 505 | 8 | 0 | |t Finding RNA-protein interaction sites using HMMs |r Tao Wang, Jonghyun Yun, Yang Xie, and Guanghua Xiao |
| 505 | 8 | 0 | |t Automated estimation of mouse social behaviors based on a hidden markov model |r Toshiya Arakawa, Akira Tanave, Aki Takahashi, Satoshi Kakihara, Tsuyoshi Koide, and Takashi Tsuchiya |
| 505 | 8 | 0 | |t Modeling movement primitives with hidden markov models for robotic and biomedical appplications |r Michelle Karg and Dana Kulic. |
| 505 | 8 | 0 | |t HMMs in protein fold classification |r Christos Lampros, Costas Papaloukas, Themis Exarchos, and Dimitrios I. Fotiadis |
| 505 | 8 | 0 | |t Application of hidden markov models in biomolecular simulations |r Saurabh Shukla, Zahra Shamsi, Alexander S. Moffett, Balaji Selvam, and Diwakar Shukla |
| 505 | 8 | 0 | |t Predicting beta barrel transmembrane proteins using HMMs |r Georgios N. Tsaousis, Stavros J. Hamodrakas, and Pantelis G. Bagos |
| 505 | 8 | 0 | |t Predicting Alpha Helical Transmembrane Proteins Using HMMs |r Georgios N. Tsaousis, Margarita C. Theodoropoulou, Stavros J. Hamodrakas, and Pantelis G. Bagos |
| 505 | 8 | 0 | |t Self-organizing hidden markov model map (SOHMMM) : biological sequence clustering and cluster visualization |r Christos Ferles, William-Scott Beaufort, and Vanessa Ferle |
| 505 | 8 | 0 | |t Analyzing single molecule FRET trajectories using HMM |r Kenji Okamoto |
| 505 | 8 | 0 | |t Modelling ChIP-seq data using HMMs |r Veronica Vinciotti |
| 505 | 8 | 0 | |t Hidden markov models in bioinformatics : SNV inference from next generation sequence |r Jiawen Bian and Xiaobo Zhou |
| 505 | 8 | 0 | |t Computationally tractable multivariate HMM in genome-wide mapping studies |r Hyungwon Choi, Debashis Ghosh, and Zhaohui Qin |
| 505 | 8 | 0 | |t Hidden markov models in population genomics |r Julien Y. Dutheil |
| 505 | 8 | 0 | |t Differential gene expression (DEX) and alternative splicing events (ASE) for temporal dynamic processes using HMMs and hierarchical bayesian modeling approaches |r Sunghee Oh and Seongho Song |
| 505 | 8 | 0 | |t Finding RNA-protein interaction sites using HMMs |r Tao Wang, Jonghyun Yun, Yang Xie, and Guanghua Xiao |
| 505 | 8 | 0 | |t Automated estimation of mouse social behaviors based on a hidden markov model |r Toshiya Arakawa, Akira Tanave, Aki Takahashi, Satoshi Kakihara, Tsuyoshi Koide, and Takashi Tsuchiya |
| 505 | 8 | 0 | |t Modeling movement primitives with hidden markov models for robotic and biomedical appplications |r Michelle Karg and Dana Kulic. |
| 650 | 0 | |a Bioinformatics |v Laboratory manuals | |
| 650 | 0 | |a Hidden Markov models |v Laboratory manuals | |
| 650 | 2 | |a Markov Chains | |
| 650 | 2 | |a Models, Biological | |
| 650 | 2 | |a Models, Statistical | |
| 650 | 2 | |a Pattern Recognition, Automated |x methods | |
| 650 | 2 | |a Sequence Analysis |x methods | |
| 655 | 2 | |a Laboratory Manuals | |
| 700 | 1 | |a Westhead, David R. |e herausgeberin |4 edt | |
| 700 | 1 | |a Vijayabaskar, M. S. |e herausgeberin |4 edt | |
| 710 | 2 | |a Springer Science + Business Media LLC |e Verlag |0 (DE-588)1065492340 |0 (DE-627)816192375 |0 (DE-576)425241297 |4 pbl | |
| 776 | 1 | |z 9781493967537 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |t Hidden Markov Models |d New York, NY : Springer New York, 2017 |h Online-Ressourcen |w (DE-101)1126035920 |
| 776 | 0 | 8 | |i Elektronische Reproduktion |z 9781493967537 |
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