ArticleIEEE journal of biomedical and health informatics2020
Inferring Gene Regulatory Networks of Metabolic Enzymes Using Gradient Boosted Trees.
Article in IEEE journal of biomedical and health informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- Network-Driven Insights into Plant Immunity: Integrating Transcriptomic and Proteomic Approaches in Plant-Pathogen Interactions.International journal of molecular sciences · 2026Review
- Harnessing Risk Assessment for Thrombosis and Bleeding to Optimize Anticoagulation Strategy in Nonvalvular Atrial Fibrillation.Thrombosis and haemostasis · 2025Article
- Review
- Artificial Intelligence-Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study.Journal of medical Internet research · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Metabolic reprogramming is a hallmark of cancer. In cancer cells, transcription factors (TFs) govern metabolic reprogramming through abnormally increasing or decreasing the transcription rate of metabolic enzymes, which provides cancer cells growth advantages and concurrently leads to the altered metabolic phenotypes observed in many cancers. Consequently, targeting TFs that govern metabolic reprogramming can be highly effective for novel cancer therapeutics. In this paper, we present TFmeta, a machine learning approach to uncover TFs that govern reprogramming of cancer metabolism. Our approach achieves the state-of-the-art performance in reconstructing relations between TFs and their target genes on public benchmark datasets. Leveraging TF binding profiles inferred from genome-wide ChIP-seq experiments and 150 RNA-seq samples from 75 paired cancerous and non-cancerous human lung tissues, our approach predicted 19 key TFs that may be the major regulators of the gene expression changes of metabolic enzymes of the central metabolic pathway glycolysis, which may underlie the dysregulation of glycolysis in non-small-cell lung cancer patients.
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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.