Mitra, Sushmita; Datta, Sujay; Perkins, Theodore; Michailidis, George

... 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

• 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.

Mitra, Sushmita; Datta, Sujay; Perkins, Theodore; Michailidis, George

... 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

• 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.

Mitra, Sushmita; Datta, Sujay; Perkins, Theodore; Michailidis, George

... 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

• 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.