Proper integration of feature subsets boosts GO subcellular localization predictions

  • Flavio Ezequiel Spetale Cifasis-Conicet
  • Elizabeth Tapia
  • Javier Murillo
  • Flavia Krsticevic
  • Sergio Ponce
  • Laura Angelone
  • Pilar Bulacio


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