Statistical Modeling and Machine Learning for Molecular Biology
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A01=Alan Moses
analysis
Author_Alan Moses
bioinformatics methods
Category=PBWH
Category=PSD
Category=UYQM
Decision Boundary
DNA Sequence Motif
enrichment
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eq_computing
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eq_isMigrated=2
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function
Gaussian Mixture Model
Gaussian mixture models
gene
Gene Expression Levels
Gene Set Enrichment Analysis
Graphical Models Representation
GWAS
hidden Markov models
high
hypothesis
Infinite Mixture Models
Lev El
Levels
linear classification
Linear Regression
Mixture Model
modern
mRNA Abundance
multivariate analysis
Null Hypothesis
objective
PL
population genetics
Posterior Probability
Quantitative Trait Loci
Reduce
Regression Model
Roc Curve
set
Simple Linear Regression
statistical techniques for biological data
Support Vector Machines
T-cell Receptor
Tr Ue
Transcription Factor Motifs
Product details
- ISBN 9781482258592
- Weight: 408g
- Dimensions: 156 x 234mm
- Publication Date: 15 Dec 2016
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Paperback
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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.
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.
