Application of hierarchical function prediction in Solanum Lycopersicum

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

Paraná

Congreso; VI Latin American Conference on Biomedical Engineering; 2014

Sociedad Argentina de Bioingeniería - Facultad de Ingeniería de la Universidad Nacional de Entre Ríos

Predicting functional gene annotations through machine learning techniques may focus on the experimental validation reducing their cost. The hierarchical prediction method based on True Path Rule provides function results consistent and traceable with the Gene Ontology Molecular Function definition. In this work, a design of a hierarchical prediction model based on True Path Rule for plants is presented. The training stage is done with \textit{Arabidopsis thaliana} data characterized with sequence domains and physicochemical properties feeding an ensemble of binary classifiers, one classifier for each functional class. The proposed model is validated against a set of well-known control sequences and with a set of sequences of \textit{S. lycopersicum} without any annotation by biological experts. The discussed results are promising; the proposal can be enriched with more organisms and with diverse sources of sequence characterizations.