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Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology.
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Previews available in: English
Subjects
Molecular biology, Mathematical models, Markov processes, Neural networks (Computer science), Computer simulation, Bioinformatics, Machine learning, Artificial Intelligence, Computer Neural Networks, Theoretical Models, Markov Chains, Computational Biology, Methods, Bio-informatique, Biologie moléculaire, Simulation par ordinateur, Modèles mathématiques, Réseaux neuronaux (Informatique), Apprentissage automatique, Processus de Markov, SCIENCE, Life Sciences, Computers & the internet, Computer engineering, Biology & life sciences, Robotics & artificial intelligence, Mathematics, Computers - general & miscellaneous, Biology, Mathematical modelingEdition | Availability |
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Bioinformatics: the machine learning approach
2001, MIT Press
in English
- 2nd ed.
026202506X 9780262025065
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- Created August 17, 2024
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