ReviewEpigenetics2017
Machine learning for epigenetics and future medical applications.
Review in Epigenetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 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
54 citing papers in PubMed.
- Machine learning-based prognostic model and single-cell transcriptomic integration for identifying brain metastasis-associated malignant subpopulations and potential therapeutic targets in lung adenocarcinoma.Cancer cell international · 2026Article
- Integrative analysis of scRNA-seq and bulk RNA-seq with machine learning develops a nucleotide metabolism-based prognostic model for ccRCC and reveals the function of IFI30.Frontiers in oncology · 2026Article
- Article
- Decoding cell-class specific roles of non-coding variants in human retina.Scientific reports · 2025Article
- Epigenetic Alterations Induced by Smoking and Their Intersection with Artificial Intelligence: A Narrative Review.International journal of environmental research and public health · 2025Review
- Exploration of heterogeneity and recurrence signatures in hepatocellular carcinoma.Molecular oncology · 2025Article
- Epigenomic insights and computational advances in hematologic malignancies.Molecular cytogenetics · 2025Review
- Machine Learning Prediction of Non-Coding Variant Impact in Cell-Class-Specific Human RetinalbioRxiv : the preprint server for biology · 2025Article
- Decoding the Methylome: Insights into the Epigenetic Regulation of Polycystic Ovarian Syndrome through Mitochondrial DNA Methylation.Current genomics · 2025Review
- Foundation models in plant molecular biology: advances, challenges, and future directions.Frontiers in plant science · 2025Review
- Generational stability of environmentally induced epigenetic transgenerational inheritance of adult-onset disease over ten mammalian generations.Environmental epigenetics · 2025Article
- Introducing artificial intelligence and sperm epigenetics in the fertility clinic: a novel foundation for diagnostics and prediction modelling.Frontiers in reproductive health · 2025Article
- Article
- Single-cell sequencing reveals novel proliferative cell type: a key player in renal cell carcinoma prognosis and therapeutic response.Clinical and experimental medicine · 2024Article
- A miRNA-based epigenetic molecular clock for biological skin-age prediction.Archives of dermatological research · 2024Article
- A novel artificial intelligence network to assess the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features.Frontiers in immunology · 2024Article
- Identification of Novel Diagnostic Biomarkers and Classification Patterns for Osteoarthritis by Analyzing a Specific Set of Genes Related to Inflammation.Inflammation · 2023Article
- Hybrid deep learning approach to improve classification of low-volume high-dimensional data.BMC bioinformatics · 2023Article
- Analysis of super-enhancer using machine learning and its application to medical biology.Briefings in bioinformatics · 2023Review
- Early antidepressant treatment response prediction in major depression using clinical and TPH2 DNA methylation features based on machine learning approaches.BMC psychiatry · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review.
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What OpenQuestion 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 OpenQuestion graph.