Statistical Modeling and Machine Learning for Molecular Biology, 9781482258592
Paperback
The book covers several of the major data analysis techniques used to analyze data from high-throughput molecular biology and genomics experiments. It also explains the major concepts behind most of the popular techniques and examines some of the simpler techniques in detail.

Statistical Modeling and Machine Learning for Molecular Biology

$112.80

  • Paperback

    264 pages

  • Release Date

    15 December 2016

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Summary

Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and…

Book Details

ISBN-13:9781482258592
ISBN-10:1482258595
Series:Chapman & Hall/CRC Computational Biology Series
Author:Alan Moses
Publisher:Taylor & Francis Inc
Imprint:Chapman & Hall/CRC
Format:Paperback
Number of Pages:264
Release Date:15 December 2016
Weight:408g
Dimensions:234mm x 156mm
About The Author

Alan Moses

Alan M Moses is currently Associate Professor and Canada Research Chair in Computational Biology in the Departments of Cell & Systems Biology and Computer Science at the University of Toronto. His research touches on many of the major areas in computational biology, including DNA and protein sequence analysis, phylogenetic models, population genetics, expression profiles, regulatory network simulations and image analysis.

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