ArticleBioMed research international2016
Modeling Gene Regulation in Liver Hepatocellular Carcinoma with Random Forests.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
The trial behind it
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
6 citing papers in PubMed, 11 citations in OpenAlex.
- STLBRF: an improved random forest algorithm based on standardized-threshold for feature screening of gene expression data.Briefings in functional genomics · 2025Article
- Genetic and Epigenetic Mechanisms Deregulate the CRL2Frontiers in genetics · 2022Article
- Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.Briefings in bioinformatics · 2021Article
- Circulating microRNA trafficking and regulation: computational principles and practice.Briefings in bioinformatics · 2020Review
- Development and Evaluation of Novel Statistical Methods in Urine Biomarker-Based Hepatocellular Carcinoma Screening.Scientific reports · 2018Article
- Comparative Analysis and Classification of Cassette Exons and Constitutive Exons.BioMed research international · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liver hepatocellular carcinoma (HCC) remains a leading cause of cancer-related death. Poor understanding of the mechanisms underlying HCC prevents early detection and leads to high mortality. We developed a random forest model that incorporates copy-number variation, DNA methylation, transcription factor, and microRNA binding information as features to predict gene expression in HCC. Our model achieved a highly significant correlation between predicted and measured expression of held-out genes. Furthermore, we identified potential regulators of gene expression in HCC. Many of these regulators have been previously found to be associated with cancer and are differentially expressed in HCC. We also evaluated our predicted target sets for these regulators by making comparison with experimental results. Lastly, we found that the transcription factor E2F6, one of the candidate regulators inferred by our model, is predictive of survival rate in HCC. Results of this study will provide directions for future prospective studies in HCC.
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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.