ArticleGenes2019
A High-Performance Computing Implementation of Iterative Random Forest for the Creation of Predictive Expression Networks.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Exploring the role of plant lysin motif receptor-like kinases in regulating plant-microbe interactions in the bioenergy cropComputational and structural biotechnology journal · 2023Article
- Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts.Frontiers in psychiatry · 2023Article
- Interpretable machine learning for genomics.Human genetics · 2022Article
- Concrete Strength Prediction Using Different Machine Learning Processes: Effect of Slag, Fly Ash and Superplasticizer.Materials (Basel, Switzerland) · 2022Article
- Provable Boolean interaction recovery from tree ensemble obtained via random forests.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- NF-κB perturbation reveals unique immunomodulatory functions in Prx1Science translational medicine · 2022Article
- Evaluating the performance of random forest and iterative random forest based methods when applied to gene expression data.Computational and structural biotechnology journal · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As time progresses and technology improves, biological data sets are continuously increasing in size. New methods and new implementations of existing methods are needed to keep pace with this increase. In this paper, we present a high-performance computing (HPC)-capable implementation of Iterative Random Forest (iRF). This new implementation enables the explainable-AI eQTL analysis of SNP sets with over a million SNPs. Using this implementation, we also present a new method, iRF Leave One Out Prediction (iRF-LOOP), for the creation of Predictive Expression Networks on the order of 40,000 genes or more. We compare the new implementation of iRF with the previous R version and analyze its time to completion on two of the world's fastest supercomputers, Summit and Titan. We also show iRF-LOOP's ability to capture biologically significant results when creating Predictive Expression Networks. This new implementation of iRF will enable the analysis of biological data sets at scales that were previously not possible.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.