Open Access
Bisociative knowledge discovery for microarray data analysis
Igor Mozetič,Nada Lavrač,Vid Podpečan,Petra Kralj Novak,Helena Motaln,Marko Petek,Kristina Gruden,Hannu Toivonen,Kimmo Kulovesi +8 more
- 01 Jan 2010
- pp 190-199
TL;DR: It is shown how enriched gene sets are found by using ontology information as background knowledge in semantic subgroup discovery by contextualized by the computation of probabilistic links to diverse bioinformatics resources.
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Abstract: The paper presents an approach to computational knowledge discovery through the mechanism of bisociation. Bisociative reasoning is at the heart of creative, accidental discovery (e.g., serendipity), and is focused on finding unexpected links by crossing contexts. Contextualization and linking between highly diverse and distributed data and knowledge sources is therefore crucial for the implementation of bisociative reasoning. In the paper we explore these ideas on the problem of analysis of microarray data. We show how enriched gene sets are found by using ontology information as background knowledge in semantic subgroup discovery. These genes are then contextualized by the computation of probabilistic links to diverse bioinformatics resources. Preliminary experiments with microarray data illustrate the approach.
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Citations
Using ontologies in semantic data mining with SEGS and g-SEGS
Nada Lavrač,Anže Vavpetič,Larisa N. Soldatova,Igor Trajkovski,Petra Kralj Novak +4 more
- 05 Oct 2011
TL;DR: The use of prototype semantic datamining systems SEGS and g-SEGS is demonstrated in a simple semantic data mining scenario and in two reallife functional genomics scenarios of mining biological ontologies with the support of experimental microarray data.
38
Live and learn from mistakes: A lightweight system for document classification
TL;DR: The 3LM algorithm did not show over-fitting, while consistently outperforming centroid-based, Naive Bayes, C4.5, AdaBoost, kNN, and SVM whose accuracy had been reported on the same three corpora.
27
Application Domains Considered in Computational Creativity.
Róisín Loughran,Michael O'Neill +1 more
- 01 Jan 2017
TL;DR: A review of papers presented at IJWCC and ICCC is presented, specifically considering what applications these papers are engaged with, either directly in generative systems or indirectly in evaluation or framework proposals.
Lexical Creativity from Word Associations
Oskar Gross,Hannu Toivonen,Jukka M. Toivanen,Alessandro Valitutti +3 more
- 08 Nov 2012
TL;DR: This paper designs minimally supervised methods that can perform well in the remote associates test (RAT), a well-known psychometric measure of creativity, and develops methods for a more general word association model that could be used in lexical creativity support systems, and which also could be a small step towards Lexical creativity in computers.
•Proceedings Article
Constraint-Based Mining of Sets of Cliques Sharing Vertex Properties
Pierre-Nicolas Mougel,Marc Plantevit,Christophe Rigotti,Olivier Gandrillon,Jean-François Boulicaut +4 more
- 20 Sep 2010
TL;DR: A method to compute all maximal homogeneous clique sets that satisfy user-defined constraints on the number of separated cliques, on the size of the clique, and on theNumber of labels shared by all the vertices is proposed.
References
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian,Pablo Tamayo,Vamsi K. Mootha,Sayan Mukherjee,Benjamin L. Ebert,Michael A. Gillette,Amanda G. Paulovich,Scott L. Pomeroy,Todd R. Golub,Eric S. Lander,Jill P. Mesirov +10 more
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
•Proceedings Article
Fast Algorithms for Mining Association Rules in Large Databases
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- 12 Sep 1994
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
PAGE: Parametric Analysis of Gene Set Enrichment
Seon-Young Kim,David J. Volsky +1 more
TL;DR: PAGE was statistically more sensitive and required much less computational effort than GSEA, it could identify significantly changed biological themes from micro array data irrespective of analysis methods or microarray platforms, and it was useful in comparison of multiple microarray data sets.
Expert-guided subgroup discovery: methodology and application
Dragan Gamberger,Nada Lavrač +1 more
TL;DR: The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search algorithm, using a novel parametrized definition of rule quality which is analyzed in detail.
Ranking indirect connections in literature-based discovery: The role of medical subject headings
TL;DR: The proposed techniques are tested on a published example of indirect connections between migraine and magnesium deficiency and demonstrate how the earlier results can be replicated with a more efficient and more systematic computer-aided process.
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