Introduction to Computational Biology Introduction to Computational Biology
Chapman & Hall/CRC Interdisciplinary Statistics

Introduction to Computational Biology

Maps, Sequences and Genomes

    • 164,99 €
    • 164,99 €

Descrizione dell’editore

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.

GENERE
Scienza e natura
PUBBLICATO
2018
2 maggio
LINGUA
EN
Inglese
PAGINE
448
EDITORE
CRC Press
DIMENSIONE
13,6
MB

Altri libri di Michael S. Waterman

Altri libri di questa serie

Bayesian Modeling of Spatio-Temporal Data with R Bayesian Modeling of Spatio-Temporal Data with R
2022
Mendelian Randomization Mendelian Randomization
2021
Model-based Geostatistics for Global Public Health Model-based Geostatistics for Global Public Health
2019
Statistics for Environmental Biology and Toxicology Statistics for Environmental Biology and Toxicology
2020
Parameter Redundancy and Identifiability Parameter Redundancy and Identifiability
2020
Statistical and Econometric Methods for Transportation Data Analysis Statistical and Econometric Methods for Transportation Data Analysis
2020