ArticleMethods (San Diego, Calif.)2016
Predicting protein function and other biomedical characteristics with heterogeneous ensembles.
Article in Methods (San Diego, Calif.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Prediction of future dementia among patients with mild cognitive impairment (MCI) by integrating multimodal clinical data.Heliyon · 2024Article
- A Comprehensive Youth Diabetes Epidemiological Data Set and Web Portal: Resource Development and Case Studies.JMIR public health and surveillance · 2024Article
- Improving transparency of computational tools for variant effect prediction.Nature genetics · 2024Article
- Developing better digital health measures of Parkinson's disease using free living data and a crowdsourced data analysis challenge.PLOS digital health · 2023Article
- Integrating multimodal data through interpretable heterogeneous ensembles.bioRxiv : the preprint server for biology · 2022Article
- Integrating multimodal data through interpretable heterogeneous ensembles.Bioinformatics advances · 2022Article
- A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization.JAMA network open · 2021Article
- Gene function finding through cross-organism ensemble learning.BioData mining · 2021Article
- MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data.Metabolomics : Official journal of the Metabolomic Society · 2020Article
- Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.JAMA network open · 2020Article
- Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images.Scientific reports · 2019Article
- A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection.Nature communications · 2018Article
- A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.Scientific reports · 2018Article
- Large-scale protein function prediction using heterogeneous ensembles.F1000Research · 2018Article
- LEARNING PARSIMONIOUS ENSEMBLES FOR UNBALANCED COMPUTATIONAL GENOMICS PROBLEMS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2017Article
- Prediction of Genetic Interactions Using Machine Learning and Network Properties.Frontiers in bioengineering and biotechnology · 2015Review
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Authors and funding
3 authors.
Funding
Abstract
Prediction problems in biomedical sciences, including protein function prediction (PFP), are generally quite difficult. This is due in part to incomplete knowledge of the cellular phenomenon of interest, the appropriateness and data quality of the variables and measurements used for prediction, as well as a lack of consensus regarding the ideal predictor for specific problems. In such scenarios, a powerful approach to improving prediction performance is to construct heterogeneous ensemble predictors that combine the output of diverse individual predictors that capture complementary aspects of the problems and/or datasets. In this paper, we demonstrate the potential of such heterogeneous ensembles, derived from stacking and ensemble selection methods, for addressing PFP and other similar biomedical prediction problems. Deeper analysis of these results shows that the superior predictive ability of these methods, especially stacking, can be attributed to their attention to the following aspects of the ensemble learning process: (i) better balance of diversity and performance, (ii) more effective calibration of outputs and (iii) more robust incorporation of additional base predictors. Finally, to make the effective application of heterogeneous ensembles to large complex datasets (big data) feasible, we present DataSink, a distributed ensemble learning framework, and demonstrate its sound scalability using the examined datasets. DataSink is publicly available from https://github.com/shwhalen/datasink.
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