ArticleF1000Research2018
Large-scale protein function prediction using heterogeneous ensembles.
Article in F1000Research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed.
- 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
- Gene function finding through cross-organism ensemble learning.BioData mining · 2021Article
- PhotoModPlus: A web server for photosynthetic protein prediction from genome neighborhood features.PloS one · 2021Article
- The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.Genome biology · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.