Wang, LinhuaLaw, Jeffrey N.Kale, Shiv D.Murali, T. M.Pandey, Gaurav2020-11-032020-11-032018-09-28Wang 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 https://doi.org/10.12688/f1000research.16415.1http://hdl.handle.net/10919/100775Heterogeneous 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 (https://github.com/GauravPandeyLab/LargeGOPred).16 pagesapplication/pdfenCreative Commons Attribution 4.0 Internationalprotein function predictionheterogeneous ensemblesMachine learninghigh-performance computingperformance evaluationArticle - Refereedhttps://doi.org/10.12688/f1000research.16415.171577