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dc.contributor.authorBaldi, Pierre
dc.contributor.authorBrunak, Soren
dc.date.accessioned2020-11-26T04:25:09Z
dc.date.available2020-11-26T04:25:09Z
dc.date.issued2001
dc.identifier.citationBaldi, Pierre and Brunak, Soren (2001). Bioinformatics: the machine learning approach. 2nd ed. Cambridge : MIT Press.en_US
dc.identifier.isbn026202506X
dc.identifier.urihttp://hdl.handle.net/123456789/1377
dc.description.abstractAn unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.en_US
dc.language.isoenen_US
dc.publisherMIT Pressen_US
dc.subjectBioinformaticsen_US
dc.subjectMolecular biology—Computer simulation.en_US
dc.subjectMolecular biology—Mathematical modelsen_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectMachine learningen_US
dc.subjectMarkov processesen_US
dc.titleBioinformatics: the machine learning approachen_US
dc.typeBooken_US


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