Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data
Abstract
Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Random Forest can be replaced in this process by iterative Random Forest (iRF), which performs variable selection and boosting. Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3 (RF-LOOP). We use both synthetic and empirical networks from the Dialogue for Reverse Engineering Assessment and Methods (DREAM) Challenges by Sage Bionetworks, as well as two additional empirical networks created from Arabidopsis thaliana and Populus trichocarpa expression data.
- Authors:
-
[1];
[2];
[2];
- University of Tennessee, Knoxville, TN (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1876307
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computational and Structural Biotechnology Journal
- Additional Journal Information:
- Journal Volume: 20; Journal Issue: na; Journal ID: ISSN 2001-0370
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; random forest; iterative random forest; gene expression networks; network biology
Citation Formats
Walker, Angelica, Cliff, Ashley, Romero, Jonathon C., Shah, Manesh, Jones, Piet C., Gazolla, Joao Gabriel Felipe Machado, Jacobson, Daniel A., and Kainer, David. Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data. United States: N. p., 2022.
Web. doi:10.1016/j.csbj.2022.06.037.
Walker, Angelica, Cliff, Ashley, Romero, Jonathon C., Shah, Manesh, Jones, Piet C., Gazolla, Joao Gabriel Felipe Machado, Jacobson, Daniel A., & Kainer, David. Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data. United States. https://doi.org/10.1016/j.csbj.2022.06.037
Walker, Angelica, Cliff, Ashley, Romero, Jonathon C., Shah, Manesh, Jones, Piet C., Gazolla, Joao Gabriel Felipe Machado, Jacobson, Daniel A., and Kainer, David. Wed .
"Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data". United States. https://doi.org/10.1016/j.csbj.2022.06.037. https://www.osti.gov/servlets/purl/1876307.
@article{osti_1876307,
title = {Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data},
author = {Walker, Angelica and Cliff, Ashley and Romero, Jonathon C. and Shah, Manesh and Jones, Piet C. and Gazolla, Joao Gabriel Felipe Machado and Jacobson, Daniel A. and Kainer, David},
abstractNote = {Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data. Random Forest-Leave One Out Prediction (RF-LOOP) is a method that has been shown to be efficient at producing these gene-to-gene networks, frequently known as GEne Network Inference with Ensemble of trees (GENIE3). Random Forest can be replaced in this process by iterative Random Forest (iRF), which performs variable selection and boosting. Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3 (RF-LOOP). We use both synthetic and empirical networks from the Dialogue for Reverse Engineering Assessment and Methods (DREAM) Challenges by Sage Bionetworks, as well as two additional empirical networks created from Arabidopsis thaliana and Populus trichocarpa expression data.},
doi = {10.1016/j.csbj.2022.06.037},
journal = {Computational and Structural Biotechnology Journal},
number = na,
volume = 20,
place = {United States},
year = {Wed Jun 22 00:00:00 EDT 2022},
month = {Wed Jun 22 00:00:00 EDT 2022}
}
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