Bioinformatics: Machine Learning Methods (2nd Edition)

Author: Pierre Baldi et al. (France)
Publisher:
Publish Date: 2003-07-01
Features: The authors not only show us a and the tools to build the current biinformatics mansion, but more importantly, they guide us through the process of how to build this mansion and how to construct the "scaffolding." This is extremely important whether for repairing this mansion or for building a new one. The book introduces the main contents of machine learning methods and their applications in biological data processing. It provides a detailed explanation of the theoretical foundation of machine learning techniques—the Bayesian probability system—and, based on this, focuses on a detailed analysis of methods such as neural networks, hidden Markov models, and probabilistic graphical models in biinformatics. The book also includes a dedicated chapter on DNA microarrays and gene expression, as well as the analysis methods for related data. The book is primarily aimed at two reader groups: one is biologists and biochemists who want to understand algorithms based on data processing; the other is scholars from fields such as physics, mathematics, statistics, computer science, who want to know the applications of machine learning methods in molecular biology research.