Inference of genetic networks using random forests: Assigning different weights for gene expression data
Author:
Affiliation:
1. Faculty of Engineering, Tottori University, 4-101, Koyama-minami, Tottori 680-8552, Japan
2. Institute for Protein Research, Osaka University, 3-2, Yamadaoka, Suita, Osaka 565-0871, Japan
Abstract
Funder
Japan Society for the Promotion of Science
Publisher
World Scientific Pub Co Pte Lt
Subject
Computer Science Applications,Molecular Biology,Biochemistry
Link
https://www.worldscientific.com/doi/pdf/10.1142/S021972001950015X
Reference25 articles.
1. dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data
2. Inference of genetic networks from time-series of gene expression levels using random forests
3. Integrative random forest for gene regulatory network inference
4. Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
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