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Large-scale protein function prediction using heterogeneous ensembles.

ABSTRACT: 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 ( https://github.com/GauravPandeyLab/LargeGOPred).

SUBMITTER: Wang L

PROVIDER: S-EPMC6221071 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Large-scale protein function prediction using heterogeneous ensembles.

Wang Linhua L Law Jeffrey J Kale Shiv D SD Murali T M TM Pandey Gaurav G

F1000Research 20180928

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 ...[more]

PMID: 30450194

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