Introduction to Computational Biology
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A01=Michael S. Waterman
advanced computational genomics techniques
Alignment Score
ATP Bind Site
Author_Michael S. Waterman
bioinformatics algorithms
Category=PDE
Category=PS
Category=UYAM
Chen Stein Method
Clonable Fragments
Clone DNA
Clone Overlap
Computational Biology
DNA fragment mapping
Double Digest
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eq_computing
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eq_isMigrated=2
eq_nobargain
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evolutionary tree inference
Gene Iv
Genome Rearrangements
Interior Loop
Interval Graph
Markov Chain
Markov Renewal Processes
Minimum Free Energy Folding
molecular sequence analysis
Motif Finding
Multiple Alignment
Multiple Sequence Alignment
Multiple Sequence Analysis
Optimal Alignment
Random Clones
Restriction Enzymes
Restriction Maps
Restriction Sites
RNA Secondary Structure
RNA structure prediction
Set Partition Problem
statistical genomics methods
Statistics
Suffix Trees
Terminal Vertices
Product details
- ISBN 9780412993916
- Weight: 990g
- Dimensions: 156 x 234mm
- Publication Date: 01 Jun 1995
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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Biology is in the midst of a era yielding many significant discoveries and promising many more. Unique to this era is the exponential growth in the size of information-packed databases. Inspired by a pressing need to analyze that data, Introduction to Computational Biology explores a new area of expertise that emerged from this fertile field- the combination of biological and information sciences.
This introduction describes the mathematical structure of biological data, especially from sequences and chromosomes. After a brief survey of molecular biology, it studies restriction maps of DNA, rough landmark maps of the underlying sequences, and clones and clone maps. It examines problems associated with reading DNA sequences and comparing sequences to finding common patterns. The author then considers that statistics of pattern counts in sequences, RNA secondary structure, and the inference of evolutionary history of related sequences.
Introduction to Computational Biology exposes the reader to the fascinating structure of biological data and explains how to treat related combinatorial and statistical problems. Written to describe mathematical formulation and development, this book helps set the stage for even more, truly interdisciplinary work in biology.
