Artificial Intelligence Methods and Tools for Systems Biology / Edition 1

by W. Dubitzky, Francisco Azuaje

Overview

This book provides simultaneously a design blueprint, user guide, research agenda, and communication platform for current and future developments in artificial intelligence (AI) approaches to systems biology. It places an emphasis on the molecular dimension of life phenomena and in one chapter on anatomical and functional modeling of the brain.

As design blueprint, the book is intended for scientists and other professionals tasked with developing and using AI technologies in the context of life sciences research. As a user guide, this volume addresses the requirements of researchers to gain a basic understanding of key AI methodologies for life sciences research. Its emphasis is not on an intricate mathematical treatment of the presented AI methodologies. Instead, it aims at providing the users with a clear understanding and practical know-how of the methods. As a research agenda, the book is intended for computer and life science students, teachers, researchers, and managers who want to understand the state of the art of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. Our aim was to maintain the readability and accessibility of a textbook throughout the chapters, rather than compiling a mere reference manual. The book is also intended as a communication platform seeking to bride the cultural and technological gap among key systems biology disciplines. To support this function, contributors have adopted a terminology and approach that appeal to audiences from different backgrounds.

Product Details

ISBN-13:	9781402029592
Publisher:	Springer Netherlands
Publication date:	03/15/2005
Series:	Computational Biology , #5
Edition description:	2004
Pages:	221
Product dimensions:	6.69(w) x 9.61(h) x 0.02(d)

Table of Contents

Lazy Learning for Predictive Toxicology based on a Chemical Ontology.- QSAR Modeling of Mutagenicity on Non-Congeneric Sets of Organic Compounds.- Characterizing Gene Expression Time Series using a Hidden Markov Model.- Analysis of Large-Scale mRNA Expression Data Sets by Genetic Algorithms.- A Data-Driven, Flexible Machine Learning Strategy for the Classification of Biomedical Data.- Cooperative Metaheuristics for Exploring Proteomic Data.- Integrating Gene Expression Data, Protein Interaction Data, and Ontology-Based Literature Searches.- Ontologies in Bioinformatics and Systems Biology.- Natural Language Processing and Systems Biology.- Systems Level Modeling of Gene Regulatory Networks.- Computational Neuroscience for Cognitive Brain Functions.