Statistical Modeling and Machine Learning for Molecular Biology (Chapman & Hall/CRC Computational Biology Series) - Softcover

Moses, Alan

Softcover

ISBN 10: 1482258595 ISBN 13: 9781482258592

Publisher: Chapman and Hall/CRC, 2017

Synopsis

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 many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.

About this title

About the Author

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.

From the Back Cover

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.

Edition details

• Publisher: Chapman and Hall/CRC
• Publication date: 2017
• Language: English
• ISBN 10: 1482258595
• ISBN 13: 9781482258592
• Binding: Paperback
• Edition number: 1
• Number of pages: 264