Top-down induction of first-order logical decision trees
Artificial Intelligence
Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Predicting genetic regulatory response using classification
Bioinformatics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Inductive Logic Programming for Gene Regulation Prediction
Inductive Logic Programming
SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Fitness function based on binding and recall rate for genetic inductive logic programming
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
A new relational Tri-training system with adaptive data editing for inductive logic programming
Knowledge-Based Systems
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We present a systems biology application of ILP, where the goal is to predict the regulation of a gene under a certain condition from binding site information, the state of regulators, and additional information. In the experiments, the boosted Tilde model is on par with the original model by Middendorf et al. based on alternating decision trees (ADTrees), given the same information. Adding functional categorizations and protein-protein interactions, however, it is possible to improve the performance substantially. We believe that decoding the regulation mechanisms of genes is an exciting new application of learning in logic, requiring data integration from various sources and potentially contributing to a better understanding on a system level.