Application of hierarchical function prediction in Solanum Lycopersicum.

SPETALE, FLAVIO; ARCE, DÉBORA P; KRSTICEVIC, FLAVIA J; MURILLO, JAVIER; TAPIA, ELIZABETH; BULACIO, PILAR

Paraná, Entre Ríos

Congreso; VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014; 2014

Predicting functional gene annotations throughmachine learning techniques may focus on the experimental validationreducing their cost. The hierarchical prediction methodbased on True Path Rule provides function results consistentand traceable with the Gene Ontology Molecular Function defi-nition. In this work, a design of a hierarchical prediction modelbased on True Path Rule for plants is presented. The trainingstage is done with Arabidopsis thaliana data characterizedwith sequence domains and physicochemical properties feedingan ensemble of binary classifiers, one classifier for each functionalclass. The proposed model is validated against a set ofwell-known control sequences and with a set of sequences of S.lycopersicum without any annotation by biological experts. Thediscussed results are promising; the proposal can be enrichedwith more organisms and with diverse sources of sequence characterizations.