Proper integration of feature subsets boosts GO subcellular localization predictions

MURILLO JAVIER; ANGELONE LAURA; SPETALE FLAVIO EZEQUIEL; KRSTICEVIC FLAVIA; BULACIO PILAR; TAPIA ELIZABETH; PONCE SERGIO

Cordoba

Congreso; XXI CONGRESO ARGENTINO DE BIOINGENIERÍA - SABI 2017; 2017

Prediction of multiple subcellular localizations in proteins brings relevant information for biological function discovery. The use of computational methods based on knowledge can be a helpful starting point for guiding the costly experimental validation. In this work, we present a multilabel classifier framework to perform Gene Ontology - Cellular Component prediction focused on the improvement of two design aspects: i) the protein sequence characterization, regarding biological knowledge with experimental evidence, and ii) the error evaluation by considering a noise model inherent in real prediction frameworks.Our proposal is validated against sets of well-known protein sequences of four model organisms D. rerio, A. thaliana, S. cerevisiae and D. melanogaster.