Hidden Markov Models for Bioinformatics

Material type: Text

TextEdition: 1Description: 416ISBN:

1402001363

Subject(s):

Nucleotide sequence -Mathematical models. Nucleotide sequence -Statistical methods. Markov processes

DDC classification:

572.8/633 KOS-H

Summary: The purpose of this book is to give a thorough and systematic introduction to probabilistic modeling in bioinformatics. The book contains a mathematically strict and extensive presentation of the kind of probabilistic models that have turned out to be useful in genome analysis. Questions of parametric inference, selection between model families, and various architectures are treated. Several examples are given of known architectures (e.g., profile HMM) used in genome analysis. Audience: This book will be of interest to advanced undergraduate and graduate students with a fairly limited background in probability theory, but otherwise well trained in mathematics and already familiar with at least some of the techniques of algorithmic sequence analysis.

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Holdings
Item type	Current library	Home library	Call number	Status	Date due	Barcode
Book	Dept. of Computational Biology and Bioinformatics Processing Center	Dept. of Computational Biology and Bioinformatics	572.8/633 KOS-H (Browse shelf(Opens below))	Available		DCB293

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Previous	572.8/633 BIO Bioinformatics - A Practical guide to the Analysis of Gene	572.8/633 CAU-M Microarray Gene Expression Data Analysis	572.8/633 GEN Genomics And Proteomics: Functional And Computational Aspects	572.8/633 KOS-H Hidden Markov Models for Bioinformatics	572.8/6330285 AGO-P Practical Bioinformatics	572.8/6330285 AGO-P Practical Bioinformatics	572.8/6330285 AGO-P Practical Bioinformatics	Next

The purpose of this book is to give a thorough and systematic introduction to probabilistic modeling in bioinformatics. The book contains a mathematically strict and extensive presentation of the kind of probabilistic models that have turned out to be useful in genome analysis. Questions of parametric inference, selection between model families, and various architectures are treated. Several examples are given of known architectures (e.g., profile HMM) used in genome analysis. Audience: This book will be of interest to advanced undergraduate and graduate students with a fairly limited background in probability theory, but otherwise well trained in mathematics and already familiar with at least some of the techniques of algorithmic sequence analysis.

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