Introduction to Machine Learning and Bioinformatics
1st Edition
Published on 5. June 2008
Book
Hardback
384 pages
978-1-58488-682-2 (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.
Reviews / Votes
... The stated audience for this book is M.S. and Ph.D. students in bioinformatics, machine intelligence, applied statistics, biostatistics, computer science, and related areas. ... a well-written collection from multiple authors that I recommend for the intended audience. Several chapters include exercises. -Technometrics, November 2009, Vol. 51, No. 4 ...a good text/reference book that summarizes the latest developments in the interface between bioinformatics and machine learning and offer[s] a thorough introduction to each field. ... One of the strengths of this book is the clear notation with a mathematical and statistical flavor, which will be attractive to Biometrics readers, especially to those new to statistical learning and data mining. It is also very readable for a variety of interested learners, researchers, and audiences from various backgrounds and disciplines. ... -Biometrics, March 2009 ... a well-structured book that is a good starting point for machine learning in bioinformatics. ... Using many popular examples, the statistical theory becomes comprehensible and bioinformatics examples motivate [readers] to apply the concepts to real data. -Markus Schmidberger, Journal of Statistical Software, November 2008More details
Language
English
Place of publication
Oxford
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Professional Practice & Development
Illustrations
19 s/w Tabellen, 1 s/w Photographie bzw. Rasterbild, 62 s/w Abbildungen
19 Tables, black and white; 1 Halftones, black and white; 62 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 25 mm
Weight
743 gr
ISBN-13
978-1-58488-682-2 (9781584886822)
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
Book
09/2019
1st Edition
Chapman & Hall/CRC
€96.70
Shipment within 15-20 days
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
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.