A Review of Ensemble Methods in Bioinformatics
Yee Hwa Yang,
Bing B. Zhou,
Albert Y. Zomaya.
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.
Keywords: Ensemble learning, bioinformatics, microarray, mass spectrometry-based proteomics, gene-gene interaction, regulatory elements prediction, ensemble of support vector machines, meta ensemble, ensemble feature selection, biology, proteins, mass spectrometry, DNA, protein-protein interactions, proteomics, POPULAR ENSEMBLE METHODS, binary dataset, polymorphism, Dettling, LogitBoost, Multiboost, diseases, biologists, Glycosylation, phosphorylation, hydrophobicity, van der Waals volume, polarity, polarizability, pseudo-amino acid, empirical evaluation, base, data-level perturbation, aggregating, clustering, high-dimensional data, SNP interaction, biomarker
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