A Review of Ensemble Methods in Bioinformatics

By Pengyi Yang, Y. H. Yang, Bing Bing Zhou, Albert Y. Zomaya. Current Bioinformatics, 2010

497 citations recorded by the source corpus.

Ensemble learning is an intensively studies 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 complexity data structures. The aim of this article is two-fold. First, 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. Second, we try to identify and summarize future trends of ensemble methods in bioinformatics. Promising directions such as ensemble of support vector machine, meta-ensemble, and ensemble based feature selection are discussed.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/84534c6fa2dab4768617205b302bfbce9b3bb376

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Citations in the corpus

Machine Learning and Integrative Analysis of Biomedical Big Data (2019, background): “In the context of ML, ensemble learning systems play a similar role [167,168].”
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, methodology): “There are surveys on ensemble classification methods [4]–[8] and ensemble regres-”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, methodology): “These first-level models are then combined by training a second-level model that uses predictions of the first-level models as features or via a meta-predictor [37] that takes a majority vote or combines prediction weights of the first-level models [38, 39].”
Random Forests and Decision Trees (2012, methodology): “As such, the decrease in bias [19] is achieved by the boosting method.”
Ensemble deep learning: A review (2021, background): “There exist multiple surveys in the literature which mainly focus on the review of ensemble learning like learning of ensemble models in classification problems [16, 17, 18, 19], regression problems [20, 21] and clustering [22].”
Ensemble deep learning in bioinformatics (2020, unknown): “Both ensemble learning and deep learning methods have been extensively studied and reviewed in the context of bioinformatics applications 5,6 .”
CRITICAL ASSESSMENT OF AUTOMATED FLOW CYTOMETRY DATA ANALYSIS TECHNIQUES (2013, result): “Similar to other data analysis settings (see Yang et al. 25 for a review), combining results from different cell population identification methods provides improved accuracy over any individual method.”
A review of machine learning in processing remote sensing data for mineral exploration (2021, methodology): “Also, a combination of other machine or deep learning methods, which are mentioned above, could be used as an ensemble technique (Dietterich; Guan et al., 2014; Yang et al., 2010).”
Efficient Machine Learning for Big Data: A Review (2015, background): “Recent data-modeling research has shown that ensemble methods have gained much popularity as they often perform better than individual models [28][29].”