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. Nevertheless, the predictive performance of individual trees is lower than that of some recently proposed statistical learning methods. We show that ensembles of such trees are more accurate than single trees and are competitive with state-of-the-art statistical learning and functional linkage methods. Moreover, the ensemble method is computationally efficient and easy to use.


CONCLUSIONS
Our results suggest that decision tree based methods are a state-of-the-art, efficient and easy-to-use approach to ORF function prediction.
Schietgat
Leander
L
Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium. leander.schietgat@cs.kuleuven.be
Vens
Celine
C
Struyf
Jan
J
Blockeel
Hendrik
H
Kocev
Dragi
D
Dzeroski
Saso
S
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D003663:Decision Trees; D020869:Gene Expression Profiling / Q000379:methods*; D005796:Genes; D016366:Open Reading Frames / Q000235:genetics