Evidence map›Paper›PMID 32245523›Full record

ReviewClinical epigenetics2020

Machine learning and clinical epigenetics: a review of challenges for diagnosis and classification.

S Rauschert, K Raubenheimer, P E Melton, R C Huang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Clinical epigenetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
110citing papers in PubMed, 10 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

110 citing papers in PubMed, 10 syntheses or guidelines pooled it.

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  11. Review
  12. Article
  13. Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Review
  14. Article
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  16. Article
  17. Climate Change, Epigenetics, Microbiota, and Health.International journal of environmental research and public health · 2026
    Review
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  19. Article
  20. Article

50 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

S RauschertTelethon Kids Institute, University of Western Australia, Nedlands, Perth, Western Australia. Sebastian.Rauschert@telethonkids.org.au.
K RaubenheimerSchool of Medicine, Notre Dame University, Fremantle, Western Australia.
P E MeltonCentre for Genetic Origins of Health and Disease, The University of Western Australia and Curtin University, Perth, Western Australia.
R C HuangTelethon Kids Institute, University of Western Australia, Nedlands, Perth, Western Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMachine learning is a sub-field of artificial intelligence, which utilises large data sets to make predictions for future events. Although most algorithms used in machine learning were developed as far back as the 1950s, the advent of big data in combination with dramatically increased computing power has spurred renewed interest in this technology over the last two decades. MAIN BODY: Within the medical field, machine learning is promising in the development of assistive clinical tools for detection of e.g. cancers and prediction of disease. Recent advances in deep learning technologies, a sub-discipline of machine learning that requires less user input but more data and processing power, has provided even greater promise in assisting physicians to achieve accurate diagnoses. Within the fields of genetics and its sub-field epigenetics, both prime examples of complex data, machine learning methods are on the rise, as the field of personalised medicine is aiming for treatment of the individual based on their genetic and epigenetic profiles.

conclusionWe now have an ever-growing number of reported epigenetic alterations in disease, and this offers a chance to increase sensitivity and specificity of future diagnostics and therapies. Currently, there are limited studies using machine learning applied to epigenetics. They pertain to a wide variety of disease states and have used mostly supervised machine learning methods.

Indexed as

DiagnosisEpigenomicsMachine LearningDiseaseDNA MethylationEpigenesis, GeneticHumansPrecision MedicineSupervised Machine LearningUnsupervised Machine Learning

Identifiers

PMID32245523
PMCPMC7118917

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.