Authors:
Péter Marx
1
;
Bence Bolgár
2
;
András Gézsi
3
;
Attila Gulyás-Kovács
2
and
Péter Antal
2
Affiliations:
1
Budapest University of Technology and Economics and MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungary
;
2
Budapest University of Technology and Economics, Hungary
;
3
Semmelweiss University, Hungary
Keyword(s):
microRNA, microRNA Target, Kernel Methods, Multiple Kernel Learning, Gene Prioritization.
Related
Ontology
Subjects/Areas/Topics:
Bioinformatics
;
Biomedical Engineering
;
Data Mining and Machine Learning
;
Pattern Recognition, Clustering and Classification
;
Systems Biology
;
Transcriptomics
Abstract:
microRNAs form a complex regulatory network with thousands of target genes. This network is known to
suffer specific, but largely elusive, genetic perturbations in various types of disease. Accurate prioritization
of microRNAs for each disease type would elucidate those perturbations and so facilitate therapeutic and
diagnostic design. The multiple target profiles of microRNAs stemming from various experimental and in
silico methods allow the definition of wide range of similarities over microRNAs, but the combined use of
these of heterogeneous similarities was not utilized in the gene prioritization approach. Using microRNAs
as bases, prioritization with a disease-specific query set of microRNAs is straightforward once a microRNAmicroRNA
similarity matrices have been derived. Here we demonstrate the application of a one-class version
of the multiple kernel learning framework in order to fuse heterogeneous characteristics of microRNAs. We
evaluate the method with breast cancer-specif
ic queries, illustrate its technological aspects, and validate our
results not only by standard leave-one-out cross validation, but also with a prospective evaluation.
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