Introduction to Machine Learning and Bioinformatics
Autor Sushmita Mitra, Sujay Datta, Theodore Perkins, George Michailidisen Limba Engleză Paperback – 19 sep 2019
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.
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Specificații
ISBN-13: 9780367387235
ISBN-10: 0367387239
Pagini: 384
Dimensiuni: 156 x 234 x 20 mm
Greutate: 0.45 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
ISBN-10: 0367387239
Pagini: 384
Dimensiuni: 156 x 234 x 20 mm
Greutate: 0.45 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Public țintă
Professional Practice & DevelopmentCuprins
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.
Notă biografică
Mitra, Sushmita; Datta, Sujay; Perkins, Theodore; Michailidis, George
Descriere
Examining the connections between these two increasingly intertwined areas, this text presents a unifying, thorough, and accessible introduction to the basic ideas and latest developments in machine learning and bioinformatics. It describes the major problems in bioinformatics and the concepts and algorithms of machine learning. The authors demonstrate the capabilities of key machine learning techniques, such as hidden Markov models and artificial neural networks, and apply state-of-the-art techniques to bioinformatics problems in structural biology, cancer treatment, and proteomics. They also include exercises at the end of some chapters and offer instructional materials on their website.