Methods for Computational Gene Prediction
Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. Get and download textbook Methods for Computational Gene Prediction for free
Methods for Computational Gene Prediction By William H. Majoros Published by Cambridge University Press Not quite what you're looking for? See more books in: Biochemistry or Biological & Life Sciences (all) Free UK delivery... on everything! See books in:
The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, Methods for Computational Gene Prediction new edition
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Methods for Computational Gene Prediction Cambridge University Press 9780521706940 09780521706940
Methods for Computational Gene Prediction , ISBN-13: 9780521706940, ISBN-10: 0521706947
This advanced text describes in detail the algorithms and models used to identify genes in genomic DNA sequences. It provides the underlying theory of both established techniques and also methods at the forefront of current research and is ideal for use in a first course in bioinformatics or computational biology.
This advanced text describes in detail the algorithms and models used to identify genes in genomic DNA sequences. It provides the underlying theory of both established techniques and also methods at the forefront of current research and is ideal for use in a first course in bioinformatics or computational biology.
Methods for Computational Gene Prediction Textbook
Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided,
