
Introduction to Machine Learning and Bioinformatics
Chapman & Hall/CRC (Publisher)
1st Edition
Published on 19. September 2019
Book
Paperback/Softback
384 pages
978-0-367-38723-5 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Professional Practice & Development
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 21 mm
Weight
585 gr
ISBN-13
978-0-367-38723-5 (9780367387235)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Sushmita Mitra | Sujay Datta | Theodore Perkins
Introduction to Machine Learning and Bioinformatics
E-Book
06/2008
1st Edition
Chapman & Hall/CRC
€89.49
Available for download

Sushmita Mitra | Sujay Datta | Theodore Perkins
Introduction to Machine Learning and Bioinformatics
Book
06/2008
1st Edition
Chapman & Hall/CRC
€207.40
Shipment within 15-20 days

Sushmita Mitra | Sujay Datta | Theodore Perkins
Introduction to Machine Learning and Bioinformatics
E-Book
06/2008
Chapman and Hall
€89.99
Available for download
Persons
Mitra, Sushmita; Datta, Sujay; Perkins, Theodore; Michailidis, George
Content
Introduction. The Biology of a Living Organism. Probabilistic and Model-Based Learning. Classification Techniques. Unsupervised Learning Techniques. Computational Intelligence in Bioinformatics. Connections. Machine Learning in Structural Biology. Soft Computing in Biclustering. Bayesian Methods for Tumor Classification. Modeling and Analysis of iTRAQ Data. Mass Spectrometry Classification. Index.