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  • "Machine-learning foundations: the probabilistic framework. Probabilistic modeling and inferrence: examples. Machine learning algorithms. Neural networks: the theory and applications. Hidden Markov models: the theory and applications. Probabilistic graphical models in bioinformatics. Probabilistic models of evolution: phylogenetic trees. Stochastic grammars and linguistics. Microarrays and gene expression. Internet resources and publica databases. Statistics. Information theory, entropy and relative entropy. Probabilistic graphical models. HMM technicalities, scaling, periodic architectures, state functions, and Dirichlet mixtures. Gaussian processes, kernel methos, and support vector machines."
  • "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."

  • "Electronic books"
  • "Electronic books"@en
  • "Livres électroniques"

  • "Bioinformatics : The machine learning approach"
  • "Bioinformatics"
  • "<&gt"@en
  • "Bioinformatics The Machine Learning Approach"@en
  • "Bioinformatics : Machine Learning Approach"
  • "Bioinformatics the machine learning approach"
  • "Bioinformatics the machine learning approach"@en
  • "Bioinformatics : the machine learning approach"
  • "Bioinformatics : the machine learning approach"@en