Machine Learning
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
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
ECML '07 Proceedings of the 18th European conference on Machine Learning
Combining subgroup discovery and permutation testing to reduce reduncancy
ISoLA'10 Proceedings of the 4th international conference on Leveraging applications of formal methods, verification, and validation - Volume Part I
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This paper presents a method that uses gene ontologies, together with the paradigm of relational subgroup discovery, to help find description of groups of genes differentially expressed in specific cancers. The descriptions are represented by means of relational features, extracted from gene ontology information, and are straightforwardly interpretable by the medical experts. We applied the proposed method to two known data sets: acute lymphoblastic leukemia (ALL) vs. acute myeloid leukemia and classification of fourteen types of cancer. Significant number of discovered groups of genes had a description, confirmed by the medical expert, which highlighted the underlying biological process that is responsible for distinguishing one class from the other classes. We view our methodology not just as a prototypical example of applying sophisticated machine learning algorithms to microarray data, but also as a motivation for developing more sophisticated functional annotations and ontologies, that can be processed by such learning algorithms.