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).
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 28, 2018
- Source ID
- 10.12688/f1000research.16415.1
Entities
People
- Gaurav Pandey
- Jeffrey N Law
- Linhua Wang
- Shiv D. Kale
- T M Murali
Organizations
- Intelligence Advanced Research Projects Activity
- International Business Machines Corporation (Armonk, NY)
- National Institutes of Health