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Machine learning in bioinformatics /
Machine learning techniques such as Markov models, support vector machines, neural networks, graphical models, etc., have been successful in analyzing life science data because of their capabilities of handling randomness and uncertainties of data and noise and in generalization. This book compiles...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, N.J. :
Wiley,
©2009.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 037 | |a 10.1002/9780470397428 |b Wiley InterScience |n http://www3.interscience.wiley.com | ||
| 050 | 4 | |a QH324.2 |b .M33 2009 | |
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| 245 | 0 | 0 | |a Machine learning in bioinformatics / |c edited by Yan-Qing Zhang, Jagath C. Rajapakse. |
| 260 | |a Hoboken, N.J. : |b Wiley, |c ©2009. | ||
| 300 | |a 1 online resource (xviii, 456 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | 0 | |t Feature selection for genomic and proteomic data mining / |r Sun-Yuan Kung and Man-Wai Mak -- |t Comparing and visualizing gene selection and classification methods for microarray data / |r Rajiv S. Menjoge and Roy E. Welsch -- |t Adaptive kernel classifiers via matrix decomposition updating for biological data analysis / |r Hyunsoo Kim and Haesun Park -- |t Bootstrapping consistency method for optimal gene selection from microarray gene expression data for classification problems / |r Shaoning Pang [and others] -- |t Fuzzy gene mining : a fuzzy-based framework for cancer microarray data analysis / |r Zhenyu Wang and Vasile Palade -- |t Feature selection for ensemble learning and its application / |r Guo-Zheng Li and Jack Y. Yang -- |t Sequence-based prediction of residue-level properties in proteins / |r Shandar Ahmad [and others] -- |t Consensus approaches to protein structure prediction / |r Dongbo Bu [and others] -- |t Kernel methods in protein structure prediction / |r Jayavardhana Gubbi, Alistair Shilton, and Marimuthu Palaniswami -- |t Evolutionary granular kernel trees for protein subcellular location prediction / |r Bo Jin and Yan-Qing Zhang -- |t Probabilistic models for long-range features in biosequences / |r Li Liao -- |t Neighborhood profile search for motif refinement / |r Chandan K. Reddy, Yao-Chung Weng, and Hsiao-Dong Chiang -- |t Markov/neural model for eukaryotic promoter recognition / |r Jagath C. Rajapakse and Sy Loi Ho -- |t Eukaryotic promoter detection based on word and sequence feature selection and combination / |r Xudong Xie, Shuanhu Wu, and Hong Yan -- |t Feature characterization and testing of bidirectional promoters in the human genome -- significance and applications in human genome research / |r Mary Q. Yang, David C. King, and Laura L. Elnitski -- |t Supervised learning methods for the microRNA studies / |r Byoung-Tak Zhang and Jin-Wu Nam -- |t Machine learning for computational haplotype analysis / |r Phil H. Lee and Hagit Shatkay -- |t Machine learning applications in SNP -- disease association study / |r Pritam Chanda, Aidong Zhang, and Murali Ramanathan -- Nanopore cheminformatics-based studies of individual molecular interactions / |r Stephen Winters-Hilt -- |t Information fusion framework for biomedical informatics / |r Srivatsava R. Ganta [and others]. |
| 520 | |a Machine learning techniques such as Markov models, support vector machines, neural networks, graphical models, etc., have been successful in analyzing life science data because of their capabilities of handling randomness and uncertainties of data and noise and in generalization. This book compiles recent approaches in machine learning, showing promise in addressing different complex bioinformatics applications from prominent researchers in the field. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Bioinformatics. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 2 | |a Computational Biology | |
| 650 | 2 | |a Medical Informatics | |
| 650 | 2 | |a Machine Learning | |
| 650 | 6 | |a Bio-informatique. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Médecine |x Informatique. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a COMPUTERS |x Bioinformatics. |2 bisacsh | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Bioinformatics |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 700 | 1 | |a Zhang, Yan-Qing. | |
| 700 | 1 | |a Rajapakse, Jagath Chandana. | |
| 758 | |i has work: |a Machine learning in bioinformatics (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFChtkRdTGJJVkk7JQT8RX |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |t Machine learning in bioinformatics. |d Hoboken, N.J. : Wiley, ©2009 |z 9780470116623 |z 0470116625 |w (DLC) 2008017095 |w (OCoLC)176898859 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=427638 |z Texto completo |
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