Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Relational Data Mining
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
SEGS: Search for enriched gene sets in microarray data
Journal of Biomedical Informatics
Decision trees for hierarchical multi-label classification
Machine Learning
Top-down induction of first-order logical decision trees
Artificial Intelligence
Decision trees for hierarchical multilabel classification: a case study in functional genomics
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Learning Relational Descriptions of Differentially Expressed Gene Groups
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
MMRF for Proteome annotation applied to human protein disease prediction
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
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Gene functions is an essential knowledge for understanding how metabolism works and designing treatments for solving malfunctions. The Modular Multi-Relational Framework (MMRF) is able to predict gene group functions. Since genes working together, it is focused on group functions rather than isolated gene functions. The approach of MMRF is flexible in several aspects, such as the kind of groups, the integration of different data sources, the organism and the knowledge representation. Besides, this framework takes advantages of the intrinsic relational structure of biological data, giving an easily biological interpretable and unique relational decision tree predicting N functions at once. This research work presents a group function prediction of S. cerevisiae (i.e.Yeast) genes grouped by protein complexes using MMRF. The results show that the predictions are restricted by the shortage of examples per class. Also, they assert that the knowledge representation is very determinant to exploit the available relational information richness, and therefore, to improve both the quantitative results and their biological interpretability.