An extended transformation approach to inductive logic programming
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Machine Learning
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
RSD: relational subgroup discovery through first-order feature construction
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
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We propose a methodology for predictive classification from gene expression data, able to combine the robustness of high-dimensional statistical classification methods with the comprehensibility and interpretability of simple logic-based models. We first construct a robust classifier combining contributions of a large number of gene expression values, and then (meta)-mine the classifier for compact summarizations of subgroups among genes associated with a given class therein. The subgroups are described by means of relational logic features extracted from publicly available gene ontology information. The curse of dimensionality pertaining to the gene expression based classification problem due to the large number of attributes (genes) is turned into an advantage in the secondary, meta-mining task as here the original attributes become learning examples. We cross-validate the proposed method on two classification problems: (i) acute lymphoblastic leukemia (ALL) vs. acute myeloid leukemia (AML), (ii) seven subclasses of ALL.