Computational Text Analysis: for functional genomics and bioinformatics by Soumya RaychaudhuriComputational Text Analysis: for functional genomics and bioinformatics by Soumya Raychaudhuri

Computational Text Analysis: for functional genomics and bioinformatics

bySoumya Raychaudhuri

Hardcover | February 26, 2006

Pricing and Purchase Info

$188.00

Earn 940 plum® points
Quantity:

Ships within 1-3 weeks

Ships free on orders over $25

Not available in stores

about

This book brings together the two disparate worlds of computational text analysis and biology and presents some of the latest methods and applications to proteomics, sequence analysis and gene expression data. Modern genomics generates large and comprehensive data sets but their interpretationrequires an understanding of a vast number of genes, their complex functions, and interactions. Keeping up with the literature on a single gene is a challenge itself-for thousands of genes it is simply impossible. Here, Soumya Raychaudhuri presents the techniques and algorithms needed to access and utilize the vast scientific text, i.e. methods that automatically "read" the literature on all the genes. Including background chapters on the necessary biology, statistics and genomics, in addition to practicalexamples of interpreting many different types of modern experiments, this book is ideal for students and researchers in computational biology, bioinformatics, genomics, statistics and computer science.
Soumya Raychaudhuri is in the Department of Genetics at Stanford University.
Loading
Title:Computational Text Analysis: for functional genomics and bioinformaticsFormat:HardcoverDimensions:312 pages, 9.69 × 6.73 × 0.92 inPublished:February 26, 2006Publisher:OUPLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198567405

ISBN - 13:9780198567400

Reviews

Table of Contents

1. An Introduction to Text Analysis in Genomics2. Functional Genomics3. Textual Profile of Genes4. Using Text in Sequence Analysis5. Using Text in the Analysis of a Gene Expression Experiment6. Analyzing Groups of Genes7. Analyzing Large Gene Expression Data Sets8. Using Text Classification for Gene Function Annotation9. Finding Gene Names10. Protein Interaction Networks11. Conlcusion