Ensemble learning is an intensively studied technique in machine learning and pattern recognition. Recent
work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in
dealing with small sample size, high-dimensionality, and complex data structures. The aim of this article is two-fold.
Firstly, it is to provide a review of the most widely used ensemble learning methods and their application in various bioinformatics
problems, including the main topics of gene expression, mass spectrometry-based proteomics, gene-gene interaction
identification from genome-wide association studies, and prediction of regulatory elements from DNA and protein
sequences. Secondly, we try to identify and summarize future trends of ensemble methods in bioinformatics. Promising
directions such as ensemble of support vector machines, meta-ensembles, and ensemble based feature selection are discussed.