Abstract

Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.

Keywords

Probabilistic logicComputer scienceHidden Markov modelSequence (biology)DNA sequencingField (mathematics)Statistical modelGrammarArtificial intelligenceComputational biologyData scienceBiologyGeneticsMathematicsDNALinguistics

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Year
1998
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book
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3228
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Richard Durbin, Sean R. Eddy, Anders Krogh et al. (1998). Biological Sequence Analysis. Cambridge University Press eBooks . https://doi.org/10.1017/cbo9780511790492

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DOI
10.1017/cbo9780511790492