Introduction to Machine Learning and Bioinformatics (Computer Science and Data Analysis)

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 &amp; 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.

Introduction to Machine Learning and Bioinformatics (Chapman & Hall/ CRC Computer Science & Data Analysis)

Business & Money

Mitra, Sushmita

Datta, Sujay

Perkins, Theodore

Michailidis, George

Economics

Chapman and Hall/CRC

State-of-the-art research in cutting-edge technologies such as nextgeneration sequencing, Hi-C chromatin capture assays, basic statistical methods for high-dimensional highthroughput data. For researchers, advanced students in contemporary statistical science. Accessible to practitioners in the biomedical sciences who are non-statisticians.

Bioinformatics Methods: From Omics to Next Generation Sequencing (Chapman & Hall/CRC Computational Biology Series)

Lin, Shili

Scholtens, Denise

Reference

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 &amp; 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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