ReviewBriefings in bioinformatics2023
Analysis of super-enhancer using machine learning and its application to medical biology.
Review in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 24 citations in OpenAlex.
- Understanding super enhancers in genome architecture and their roles in livestock breeding.BMC genomics · 2026Review
- Enhancer regulation in cancer: from epigenetics to mArchives of pharmacal research · 2025Review
- dbscATAC: a resource of single-cell super-enhancers/enhancers and gene markers derived from scATAC-seq data.Bioinformatics (Oxford, England) · 2025Article
- SEgene identifies links between super enhancers and gene expression across cell types.NPJ systems biology and applications · 2025Article
- Sequence-Only Prediction of Super-Enhancers in Human Cell Lines Using Transformer Models.Biology · 2025Article
- Super-enhancer inhibitors THZ2 and JQ1 reverse temozolomide resistance in glioblastoma by suppressing SE-driven SOX9 expression.Cancer drug resistance (Alhambra, Calif.) · 2025Article
- Super-enhancer DNA methylation in cancer: the mechanism of action and therapeutic directions.Frontiers in oncology · 2025Review
- Super-enhancers in immune system regulation: mechanisms, pathological reprogramming, and therapeutic opportunities.Frontiers in immunology · 2025Review
- Targeting super-enhancers in liver cancer: from pathogenic mechanisms to clinical applications.Frontiers in pharmacology · 2025Review
- Machine learning and network analysis with focus on the biofilm inComputational and structural biotechnology journal · 2024Article
- AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.Molecules (Basel, Switzerland) · 2024Review
- Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations.Molecular cancer · 2024Article
- Effects of super-enhancers in cancer metastasis: mechanisms and therapeutic targets.Molecular cancer · 2024Review
- Analysis of Emerging Variants of Turkey Reovirus using Machine Learning.Briefings in bioinformatics · 2024Article
- Analysis of Emerging Variants of Turkey Reovirus using Machine Learning.Briefings in bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the overexpression of oncogenes. Because the analysis of SEs and integrated analysis with other data are performed using large amounts of genome-wide data, artificial intelligence technology, with machine learning at its core, has recently begun to be utilized. In promoting precision medicine, it is important to consider information from SEs in addition to genomic data; therefore, machine learning technology is expected to be introduced appropriately in terms of building a robust analysis platform with a high generalization performance. In this review, we explain the history and principles of SE, and the results of SE analysis using state-of-the-art machine learning and integrated analysis with other data are presented to provide a comprehensive understanding of the current status of SE analysis in the field of medical biology. Additionally, we compared the accuracy between existing machine learning methods on the benchmark dataset and attempted to explore the kind of data preprocessing and integration work needed to make the existing algorithms work on the benchmark dataset. Furthermore, we discuss the issues and future directions of current SE analysis.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.