Evaluating the Performance of Random Forest and Iterative Random Forest Based Methods when Applied to Gene Expression Data

Dataset · Thu Jun 09 04:00:00 EDT 2022

DOI:https://doi.org/10.25983/1871691· OSTI ID:1871691

Walker, Angela M ^[1]; Cliff, Ashley ^[1]; Romero, Jonathon ^[1]; Shah, Manesh ^[1]; Jones, Piet ^[1]; Gazola, Joao Gabriel Felipe Machado ^[1]; Jacobson, Daniel ^[1]; Kainer, David ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

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). Here we validate that iterative Random Forest-Leave One Out Prediction (iRF-LOOP) produces higher quality networks than GENIE3. 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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1871691
• Country of Publication: United States
• Language: English

Related Subjects

iRF-Loop
expression network
Populus Trichocarpa