Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching
Machine Learning - Special issue on multistrategy learning
Extracting Context-Sensitive Models in Inductive Logic Programming
Machine Learning
Inductive logic programming for knowedge discovery in databases
Relational Data Mining
How to upgrade propositional learners to first order logic: case study
Relational Data Mining
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
Challenges for Inductive Logic Programming
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
How to Upgrade Propositional Learners to First Order Logic: A Case Study
Machine Learning and Its Applications, Advanced Lectures
An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experiment
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Experiments in Predicting Biodegradability
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
A Note on Two Simple Transformations for Improving the Efficiency of an ILP System
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
An empirical study of the use of relevance information in inductive logic programming
The Journal of Machine Learning Research
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Prospects and challenges for multi-relational data mining
ACM SIGKDD Explorations Newsletter
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Machine Learning
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Discovering frequent topological structures from graph datasets
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Classifying Chemical Compounds Using Contrast and Common Patterns
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Kernels for Chemical Compounds in Biological Screening
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Mining Frequent Connected Subgraphs Reducing the Number of Candidates
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
ILP-based concept discovery in multi-relational data mining
Expert Systems with Applications: An International Journal
Mining globally distributed frequent subgraphs in a single labeled graph
Data & Knowledge Engineering
An assessment of submissions made to the predictive toxicology evaluation challenge
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Constructive induction: a version space-based approach
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A new algorithm for mining frequent connected subgraphs based on adjacency matrices
Intelligent Data Analysis
Full duplicate candidate pruning for frequent connected subgraph mining
Integrated Computer-Aided Engineering
A quantitative comparison of the subgraph miners mofa, gspan, FFSM, and gaston
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Knowledge hiding from tree and graph databases
Data & Knowledge Engineering
Studies on rough sets in multiple tables
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Frequency concepts and pattern detection for the analysis of motifs in networks
Transactions on Computational Systems Biology III
Mining frequent correlated graphs with a new measure
Expert Systems with Applications: An International Journal
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Can an AI program contribute to scientific discovery? An area where this gauntlet has been thrown is that of understanding the mechanisms of chemical carcinogenesis. One approach is to obtain Structure-Activity Relationships (SARs) relating molecular structure to cancerous activity. Vital to this are the rodent carcinogenicity tests conducted within the US National Toxicology Program by the National Institute of Environmental Health Sciences (NIEHS). This has resulted in a large database of compounds classified as carcinogens or otherwise. The Predictive-Toxicology Evaluation project of the NIEHS provides the opportunity to compare carcinogenicity predictions on previously untested chemicals. This presents a formidable challenge for programs concerned with knowledge discovery. Desirable features of this problem are: (1) involvement in genuine scientific discovery; (2) availability of a large database with expert-certified classifications; (3) strong competition from methods used by chemists; and (4) participation in true blind trials, with results available by next IJCAI. We describe the materials and methods constituting this challenge, and provide some initial benchmarks. These show the Inductive Logic Programming tool Progol to be competitive with current state-of-the-art. The challenge described here is aimed at encouraging AI programs to avail themselves the opportunity of contributing to an enterprise with immediate scientific value.