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Zusatztext ? 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 2008 Informationen zum Autor Mitra, Sushmita; Datta, Sujay; Perkins, Theodore; Michailidis, George Klappentext 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. Zusammenfassung 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. Inhaltsverzeichnis 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....
