Statistical Modeling and Machine Learning for Molecular Biology

Author:   Alan Moses (University of Toronto, Canada)
Publisher:   Taylor & Francis Inc
ISBN:  

9781482258592


Pages:   264
Publication Date:   15 December 2016
Format:   Paperback
Availability:   In Print   Availability explained
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Statistical Modeling and Machine Learning for Molecular Biology


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Overview

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.

Full Product Details

Author:   Alan Moses (University of Toronto, Canada)
Publisher:   Taylor & Francis Inc
Imprint:   Chapman & Hall/CRC
Dimensions:   Width: 15.60cm , Height: 1.50cm , Length: 23.40cm
Weight:   0.408kg
ISBN:  

9781482258592


ISBN 10:   1482258595
Pages:   264
Publication Date:   15 December 2016
Audience:   General/trade ,  College/higher education ,  Professional and scholarly ,  General ,  Tertiary & Higher Education
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Introduction. Statistical modeling. Statistics and probability. Multiple testing. Multivariate statistics and parameter estimation. Clustering. Distance-based. Gaussian mixture models. Simple linear regression. Multiple regression and generalized linear models. Regularization. Linear classification. Non-linear classification. Evaluating classifiers and ensemble methods. Correlated data in one dimension. Hidden-Markov models. Local regression.

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Author Information

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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