1st Edition
Statistical Modeling and Machine Learning for Molecular Biology
By Alan Moses
Copyright 2017
280 Pages
50 B/W Illustrations
by
Chapman & Hall
280 Pages
by
Chapman & Hall
280 Pages
50 B/W Illustrations
by
Chapman & Hall
Also available as eBook on:
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,... Read more
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.
Biography
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.
