Introduction to Machine Learning and Bioinformatics

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A01=George Michailidis
A01=Sujay Datta
A01=Sushmita Mitra
A01=Theodore Perkins
advanced machine learning applications
Author_George Michailidis
Author_Sujay Datta
Author_Sushmita Mitra
Author_Theodore Perkins
Bayesian inference methods
Category=PSAX
Category=UYQM
computational biology
eq_bestseller
eq_computing
eq_isMigrated=1
eq_nobargain
eq_non-fiction
eq_science
graduate level bioinformatics
supervised classification
tumor data analysis
unsupervised clustering

Product details

  • ISBN 9780367387235
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 19 Sep 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Lucidly Integrates Current Activities

Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other.

Examines Connections between Machine Learning & Bioinformatics

The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website.

Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems

Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

Mitra, Sushmita; Datta, Sujay; Perkins, Theodore; Michailidis, George

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