Large-scale protein function prediction using heterogeneous ensembles


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Heterogeneous ensembles are an effective approach in scenarios where the ideal data type and/or individual predictor are unclear for a given problem. These ensembles have shown promise for protein function prediction (PFP), but their ability to improve PFP at a large scale is unclear. The overall goal of this study is to critically assess this ability of a variety of heterogeneous ensemble methods across a multitude of functional terms, proteins and organisms. Our results show that these methods, especially Stacking using Logistic Regression, indeed produce more accurate predictions for a variety of Gene Ontology terms differing in size and specificity. To enable the application of these methods to other related problems, we have publicly shared the HPC-enabled code underlying this work as LargeGOPred (



protein function prediction, heterogeneous ensembles, Machine learning, high-performance computing, performance evaluation


Wang L, Law J, Kale SD et al. Large-scale protein function prediction using heterogeneous ensembles [version 1; peer review: 2 approved] F1000Research 2018, 7(ISCB Comm J):1577