Application of Hierarchical Function Prediction in Solanum Lycopersicum

Predicting functional gene annotations through machine learning techniquesmay focus on the experimental val- idation reducing their cost. The hierarchical prediction method based on True Path Rule provides function results consistent and traceable with the Gene Ontology Molecular Function defi- nition. In this work, a design of a hierarchical prediction model based on True Path Rule for plants is presented. The train- ing stage is done with Arabidopsis thaliana data characterized with sequence domains and physicochemical properties feeding an ensemble of binary classifiers, one classifier for each func- tional 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 withmore organisms and with diverse sources of sequence char- acterizations.