Download Data analysis and visualization in genomics and proteomics by Francisco Azuaje, Joaquin Dopazo PDF

By Francisco Azuaje, Joaquin Dopazo

Facts research and Visualization in Genomics and Proteomics is the 1st e-book addressing integrative facts research and visualization during this box. It addresses very important concepts for the translation of information originating from a number of assets, encoded in several codecs or protocols, and processed via a number of structures.

  • one of many first systematic overviews of the matter of organic info integration utilizing computational techniques>
  • This e-book offers scientists and scholars with the foundation for the advance and alertness of integrative computational the right way to examine organic info on a systemic scale>
  • areas emphasis at the processing of a number of info and information assets, and the combo of other versions and structures >

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Genome Res, 14, 160–169. , Duin, R. P. , and Matas, J. (1998) On combining classifiers. IEEE Trans Pattern Anal Machine Intell, 20 (3), 226–239. Leung, Y. F. and Cavalieri, D. (2003) Fundamentals of cDNA microarray data analysis. Trends Genetics, 19 (11), 649–659. Tan, A. C. and Gilbert, D. (2003) Ensemble machine learning on gene expression data for cancer classification. Appl Bioinformatics, 2 (Suppl. 3), S75–S83. REFERENCES 39 Troyanskaya, O. , Owen, A. , Altman, R. , and Botstein, D. (2003) A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae).

Duin, R. P. , and Matas, J. (1998) On combining classifiers. IEEE Trans Pattern Anal Machine Intell, 20 (3), 226–239. Leung, Y. F. and Cavalieri, D. (2003) Fundamentals of cDNA microarray data analysis. Trends Genetics, 19 (11), 649–659. Tan, A. C. and Gilbert, D. (2003) Ensemble machine learning on gene expression data for cancer classification. Appl Bioinformatics, 2 (Suppl. 3), S75–S83. REFERENCES 39 Troyanskaya, O. , Owen, A. , Altman, R. , and Botstein, D. (2003) A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae).

2002) Relating whole-genome expression data with protein–protein interactions. Genome Research, 12 (1), 37–46. , Krogan, N. , Greenblatt, J. , and Gerstein, M. (2003) A Bayesian networks approach for predicting protein– protein interactions from genomic data. Science, 302 (17), 449–453. , Smedley, D. et al. (2004) EnsMart – a generic system for fast and flexible access to biological data. Genome Res, 14, 160–169. , Duin, R. P. , and Matas, J. (1998) On combining classifiers. IEEE Trans Pattern Anal Machine Intell, 20 (3), 226–239.

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