Predicting gene function using hierarchical multi-label decision tree ensembles

Background: S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains unclear which method is to be preferred in terms of predictive performance, efficiency and usability. Results: We study the use of decision tree based models for predicting the multiple functions of ORFs. First, we describe an algorithm for learning hierarchical multi-label decision trees. These can simultaneously predict all the functions of an ORF, while respecting a given hierarchy of gene functions (such as FunCat or GO). We present new results obtained with this algorithm, showing that the trees found by it exhibit clearly better predictive performance than the trees found by previously described methods. Neverthele...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Leander Schietgat, Celine Vens, Jan Struyf, Hendrik Blockeel, Dragi Kocev, Saso Dzeroski
• Keywords: BMCBI 2010, Decision Tree, Genome, Predictive Performance