Application of hierarchical function prediction in Solanum Lycopersicum

SPETALE FLAVIO EZEQUIEL; ARCE DEBORA; KRSTICEVIC FLAVIA; MURILLO JAVIER; TAPIA ELIZABETH; BULACIO PILAR

IFMBE PROCEEDINGS

Springer International Publishing

Año: 2015 vol. 49 p. 667 - 670

1680-0737

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 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 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.