ReviewBiochimica et biophysica acta. Reviews on cancer2021
Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine.
Review in Biochimica et biophysica acta. Reviews on cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 37 citations in OpenAlex.
- PROTACs in cancer therapy: targeted degradation of GPX4, PARP and epigenetic regulators.Journal of enzyme inhibition and medicinal chemistry · 2026Review
- Artificial Intelligence-Driven Multidimensional Phenotyping of Gut Metabolic States for Personalized Prebiotic, Probiotic, and Postbiotic Strategies.Molecular nutrition & food research · 2026Article
- Plasma circulating tumor deoxyribonucleic acid methylation enables noninvasive disease stratification beyond prostate-specific antigen in prostate cancer.Prostate international · 2026Article
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- Screening and validation of potential molecular markers for colorectal cancer: based on bioinformatics analysis and machine learning.Clinical and experimental medicine · 2026Article
- Article
- Machine learning-based prediction of permanent ileostomy in older adults after laparoscopic anterior resection for rectal cancer.Asia-Pacific journal of oncology nursing · 2025Article
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
- UNet with Attention Networks: A Novel Deep Learning Approach for DNA Methylation Prediction in HeLa Cells.Genes · 2025Article
- A cost-effective method for combining the power of genetic and epigenetic selection in animal production.Environmental epigenetics · 2025Article
- Machine learning-guided synthesis of nanomaterials for breast cancer therapy.Scientific reports · 2024Article
- Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia.Tissue engineering. Part A · 2024Review
- Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework.Scientific reports · 2024Article
- AI and ML-based risk assessment of chemicals: predicting carcinogenic risk from chemical-induced genomic instability.Frontiers in toxicology · 2024Review
- Technical Report: Machine-Learning Pipeline for Medical Research and Quality-Improvement Initiatives.Cureus · 2023Article
- Pain management in patients with hepatocellular carcinoma after transcatheter arterial chemoembolisation: A retrospective study.World journal of gastrointestinal surgery · 2023Article
- Value of genomics- and radiomics-based machine learning models in the identification of breast cancer molecular subtypes: a systematic review and meta-analysis.Annals of translational medicine · 2022Article
- Ten simple rules for organizing a special session at a scientific conference.PLoS computational biology · 2022Article
- Predicting High Blood Pressure Using DNA Methylome-Based Machine Learning Models.Biomedicines · 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
3 authors at 1 institution in 1 country.
Funding
Abstract
The recent deluge of genome-wide technologies for the mapping of the epigenome and resulting data in cancer samples has provided the opportunity for gaining insights into and understanding the roles of epigenetic processes in cancer. However, the complexity, high-dimensionality, sparsity, and noise associated with these data pose challenges for extensive integrative analyses. Machine Learning (ML) algorithms are particularly suited for epigenomic data analyses due to their flexibility and ability to learn underlying hidden structures. We will discuss four overlapping but distinct major categories under ML: dimensionality reduction, unsupervised methods, supervised methods, and deep learning (DL). We review the preferred use cases of these algorithms in analyses of cancer epigenomics data with the hope to provide an overview of how ML approaches can be used to explore fundamental questions on the roles of epigenome in cancer biology and medicine.
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Identifiers
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.