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

"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."--Jacket.

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.

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 todays biological experiments. Content: 1. Introduction -- 2. The biology of a living organism -- 3. Probabilistic and model-based learning -- 4. Classification techniques -- 5. Unsupervised learning techniques -- 6. Computational intelligence in bioinformatics -- 7. Connections between machine learning and bioinformatics -- 8. Machine learning in structural biology : interpreting 3D protein images -- 9. Soft computing in biclustering -- 10. Bayesian machine-learning methods for tumor classification using gene expression data -- 11. Modeling and analysis of quantitative proteomics data obtained from iTRAQ experiments -- 12. Statistical methods for classifying mass spectrometry database search results. Abstract: Presents an introduction to the basic ideas and developments in machine learning and bioinformatics. This book describes various problems in bioinformatics and the concepts and algorithms of machine learning. It demonstrates the capabilities of key machine learning techniques, such as hidden Markov models and artificial neural networks. � Read more... Exploring directions for future research, Introduction to Machine Learning and Bioinformatics provides a single source of information on the latest developments between these two important areas. The book describes the main problems in bioinformatics and explains the fundamental concepts and algorithms of machine learning. Illustrative examples from bioinformatics demonstrate the capabilities of state-of-the-art machine learning techniques and how they can be applied to bioinformatics problems. Suitable for self-study, the text also contains end-of-chapter problems and offers supporting materials, including datasets and PowerPoint slides, available for download on the web