Bioinformatics - Machine Learning Methods

Author: None
Publisher:
Publish Date: 2003-07-01
Features: The author not only shows us a and the tools to build the current bioinformatics mansion but also guides us through the process of constructing it, how to build the "scaffolding," which is very important 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—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 bioinformatics. The book also includes a special 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 biology and biochemistry researchers who want to understand algorithms based on data processing; the other is scholars in fields such as physics, mathematics, statistics, computer science, who want to know the applications of machine learning methods in molecular biology research.