Artificial Intelligence Methods and Tools for Systems Biology
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 an...
| Other Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Dordrecht
Springer Netherlands
2004, 2004
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| Edition: | 1st ed. 2004 |
| Series: | Computational Biology
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
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