Bioinformatics -- Machine Learning Methods: Machine Learning Methods

Author: Pierre Baldi
Publisher:
Publish Date: 2003-07-01
Features: The author not only shows us a and the tools to build the current biinformatics mansion but also guides us through the process of constructing this mansion, how to build the "scaffolding," which is crucial whether for repairing this mansion or building a new one. The book introduces the main content of machine learning methods and their applications in biological data processing. It provides a detailed explanation of the theoretical foundation of machine learning technology—Bayesian probability systems—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 dedicates a chapter to 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 in fields such as physics, mathematics, statistics, computer science, who are interested in the applications of machine learning methods in molecular biology research.