A Relational Learning Approach to Structure-Activity Relationships in Drug Design Toxicity Studies

Articles in the same Issue

1. Modelling Proteolytic Enzymes With Support Vector Machines
2. Rapid development of proteomic applications with the AIBench framework
3. Inter-dinucleotide distances in the human genome: an analysis of the whole-genome and protein-coding distributions
4. Model Reference Adaptive Scheme for Multi-drug Infusion for Blood Pressure Control
5. Parallel Niche Pareto AlineaGA – an Evolutionary Multiobjective approach on Multiple Sequence Alignment
6. Prognostic Prediction through Biclustering-Based Classification of Clinical Gene Expression Time Series
7. Improving imbalanced scientific text classification using sampling strategies and dictionaries
8. Evaluating the effect of unbalanced data in biomedical document classification
9. Classification methods for finding articles describing protein-protein interactions in PubMed
10. Bioinformatic tools help molecular characterization of Perkinsus olseni differentially expressed genes
11. Tracking T and B cells from two-photon microscopy imaging using constrained SMC clusters
12. Predicting protein distance maps according to physicochemical properties
13. A Relational Learning Approach to Structure-Activity Relationships in Drug Design Toxicity Studies
14. A Study of the Short and Long-term Regulation of E. coli Metabolic Pathways