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A Review of Ensemble Methods in Bioinformatics

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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.

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Keywords: DNA; Dettling; Ensemble learning; Glycosylation; LogitBoost; Meta Ensemble; Multiboost; POPULAR ENSEMBLE METHODS; SNP interaction; aggregating; base; binary dataset; bioinformatics; biologists; biology; biomarker; clustering; data-level perturbation; diseases; empirical evaluation; ensemble feature selection; ensemble of support vector machines; gene-gene interaction; high-dimensional data; hydrophobicity; mass spectrometry; mass spectrometry-based proteomics; meta ensemble; microarray; phosphorylation; polarity, polarizability; polymorphism; protein-protein interactions; proteins; proteomics; pseudo-amino acid; regulatory elements prediction; van der Waals volume

Document Type: Research Article

Publication date: December 1, 2010

More about this publication?
  • Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

    The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

    Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
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