Predicting gene function using hierarchical multi-label decision tree ensembles

Leander Schietgat 0 Celine Vens 0 Jan Struyf 0 Hendrik Blockeel 0 Dragi Kocev 1 Sao Deroski 1 0 Department of Computer Science, Katholieke Universiteit Leuven , Celestijnenlaan 200A, 3001 Leuven , Belgium 1 Department of Knowledge Technologies, Jozef Stefan Institute , Jamova cesta 39, 1000 Ljubljana , Slovenia 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. - Background The completion of several genome projects in the past decade has generated the full genome sequence of many organisms. Identifying open reading frames (ORFs) in the sequences and assigning biological functions to them has now become a key challenge in modern biology. This last step, which is the focus of our paper, is often guided by automatic discovery processes which interact with the laboratory experiments. More precisely, machine learning techniques are used to predict gene functions from a predefined set of possible functions (e.g., the functions in the Gene Ontology). Afterwards, the predictions with highest confidence can be tested in the lab. There are two characteristics of the function prediction task that distinguish it from common machine learning tasks: (1) a single gene may have multiple functions; and (2) the functions are organized in a hierarchy: a gene that is related to some function is automatically related to all its ancestor functions (this is called the hierarchy constraint). This particular problem setting is known in machine learning as hierarchical multi-label classification (HMC) and recently, many approaches have been proposed to deal with it [1-19]. These approaches differ with respect to a number of characteristics: which learning algorithm they are based on, whether the hierarchy constraint is always met and whether they can deal with hierarchies structured as a directed acyclic graph (DAG), such as the Gene Ontology, or are restricted to hierarchies structured as a rooted tree, like MIPSs FunCat. Decision trees are a well-known type of classifiers that can be learned efficiently from large datasets, produce accurate predictions and can lead to knowledge that provides insight in the biology behind the predictions, as demonstrated by Clare et al. [3]. They have been applied to several machine learning tasks [20]. In earlier work [14], we have investigated how they can be extended to the HMC setting: we presented an HMC decision tree learner that takes into account the hierarchy constraint and that is able to process DAG structured hierarchies. In this article, we show that our HMC decision tree method outperforms previously published approaches applied to S. cerevisiae and A. thaliana. Our comparisons primarily use precision-recall curves. This evaluation method is well-suited for the HMC tasks considered here, due to the large class skew present in these tasks. Moreover, we show that by upgrading our method to an ensemble technique, classification performance improves further. Ensemble techniques are learning methods that construct a set of classifiers and classify new data instances by taking a vote over their predictions. Experiments show that ensembles of decision trees outperform Bayesian corrected support vector machines [10], a statistical learning method for gene function prediction, on S. cerevisiae data, and methods participating in the MouseFunc challenge [21,22] on M. musculus data. Related work A number of machine learning approaches have been proposed in the area of functional genomics. They have been applied in the context of gene function prediction in S. cerevisiae, A. thaliana or M. musculus. We have grouped them according to the learning approach they use. Network based methods Several approaches predict functions of unannotated genes based on known functions of genes that are nearby in a functional association network or proteinprotein interaction network [2,4,5,8,15-17]. GENEFAS [4], for example, predicts functions of unannotated yeast genes based on known functions of genes that are nearby in a functional association network. GENEMANIA [15] calculates per gene function a composite functional association network from multiple networks derived from different genomic and proteomic data sources. These approaches are based on label propagation and do not return a global predictive model. However, a number of approaches were proposed to combine predictions of functional networks with those of a predictive model. Kim et al. [16] combine them with predictions from a Naive Bayes classifier. The combination is based on a simple aggregation function. The Funckenstein system [17] uses logistic regression to combine predictions made by a functional association network with predictions from a random forest. Kernel based methods Deng et al. [1] predict gene functions with Markov random fields using protein interaction data. They learn a model for each gene function separately and ignore the hierarchical relationships between the functions. Lanckriet et al. [6] represent the data by means of a kernel function and construct support vector machines for each gene function separately. They only predict toplevel classes in the hierarchy. Lee et al. [13] have combined the Markov random field approach of [1] with the SVM approach of [6] by computing diffusion kernels and using them in kernel logistic regression. Obozinski et al. [19] present a two-step approach in which SVMs are first learned independently for each gene function separately (allowing violations of the hierarchy constraint) and are then reconcilia (...truncated)