## Probabilistic Modeling in Bioinformatics and Medical InformaticsDirk Husmeier, Richard Dybowski, Stephen Roberts Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies. |

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### Contents

3 | |

References | 15 |

References | 55 |

References | 79 |

References | 142 |

RNABased Phylogenetic Methods | 191 |

References | 208 |

Statistical Methods in Microarray Gene Expression Data | 211 |

Bayesian Analysis of Population | 351 |

References | 369 |

References | 388 |

References | 416 |

A Model Free Update Equations | 442 |

References | 449 |

Probabilities for Sepsis and Pathogens | 456 |

References | 470 |

### Other editions - View all

Probabilistic Modeling in Bioinformatics and Medical Informatics Dirk Husmeier,Richard Dybowski,Stephen Roberts No preview available - 2010 |

### Common terms and phrases

algorithm analysis applied approximation assumed assumption Bayesian approach Bayesian networks biological bootstrap branch lengths chapter classification clinical coefficients conditional probability convergence corresponding d-separation data set defined denote density discussed in Section DNA sequence DNA sequence alignment drug edges EM algorithm equation estimate example expression data feature function Gaussian gene expression genetic given graph Graphical Models hidden Markov models hidden nodes hyperparameters illustrated in Figure infection inference input interactions latent variables learning linear Markov chain matrix maximum likelihood MCMC methods Molecular Neural Networks nucleotide nucleotide substitution observation model obtained optimization parameters phylogenetic tree posterior distribution posterior probability prediction prior probability distribution problem protein random variables recombinant regions represent sample score sequence alignment shows simulation statistical structure subfigure substitution model tion TOPAL tree topologies true uncertainty update values variance vector