Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data.
Comput Struct Biotechnol J
; 20: 3372-3386, 2022.
Article
en En
| MEDLINE
| ID: mdl-35832622
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.
GENIE3, GEne Network Inference with Ensemble of trees; GRN, Gene Regulatory Network; Gene expression networks; Iterative random forest; Network biology; PEN, Predictive Expression Network; RF, Random Forest; RF-LOOP, Random Forest Leave One Out Prediction; Random forest; iRF, iterative Random Forest; iRF-LOOP, iterativeRandom Forest Leave One Out Prediction
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Clinical_trials
/
Prognostic_studies
Idioma:
En
Revista:
Comput Struct Biotechnol J
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos