Evidence map›Paper›PMID 41736096›Full record

ArticleGenome biology2026

Reducing demographic bias in biomedical machine learning for cancer detection using cfDNA methylation.

Shuo Li, Weihua Zeng, Wenyuan Li, Chun-Chi Liu, Yonggang Zhou, Xiaohui Ni, Mary L Stackpole, Angela H Yeh, Andrew Melehy, David S Lu and 16 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

26 authors.

Shuo LiDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA. shuoli@ucla.edu.
Weihua ZengDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Wenyuan LiDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Chun-Chi LiuEarlyDiagnostics Inc., Los Angeles, CA, 90095, USA.
Yonggang ZhouDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Xiaohui NiEarlyDiagnostics Inc., Los Angeles, CA, 90095, USA.
Mary L StackpoleEarlyDiagnostics Inc., Los Angeles, CA, 90095, USA.
Angela H YehDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Andrew MelehyDepartment of Surgery, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
David S LuDepartment of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Steven S RamanDepartment of Surgery, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
William HsuDepartment of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Lopa MishraInstitute for Bioelectronic Medicine, Divisions of Gastroenterology and Hepatology, Department of Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, 11030, USA.
Kirti ShettyDepartment of Gastroenterology and Hepatology, the University of Maryland, School of Medicine, Baltimore, MD, 21201, USA.
Benjamin TranDepartment of Surgery, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Megumi YokomizoDepartment of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Preeti AhujaDepartment of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Yazhen ZhuJonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Hsian-Rong TsengJonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Denise R AberleDepartment of Radiological Sciences, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Vatche G AgopianDepartment of Surgery, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Steven-Huy B HanDepartment of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Samuel W FrenchDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Steven M DubinettDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA.
Xianghong Jasmine ZhouDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, 90095, USA. XJZhou@mednet.ucla.edu.
Wing Hung WongDepartment of Statistics, Stanford University, Stanford, CA, 94305, USA. whwong@stanford.edu.

Funding

The UCLA Center in Early Detection of Liver CancerU01CA230705 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Vatche Agopian, Samuel Wheeler French · 2018 to 2026
$6.6M
The Boston University - UCLA Lung Cancer Biomarker Characterization CenterU2CCA271898 · NCI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI MICHAEL J PALAZZOLO · 2022 to 2026
$5.5M
Multi-cancer early detection using cell-free DNA methylome analysisU01CA285010 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ROBERT S BRESALIER, Wenyuan Li · 2023 to 2026
$3.3M
Detecting and locating cancer for patients with CT-detected lung nodulesR01CA264864 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ZHOU, XIANGHONG JASMINE · 2022 to 2025
$3.2M
Novel Computation Methods for the Analysis of Cell-Free DNA Sequence DataR01CA246329 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DUBINETT, STEVEN M., LI, WENYUAN · 2019 to 2022
$2.2M
Statistical methods for gene regulatory analysis and single cell genomicsR01HG010359 · NHGRI · STANFORD UNIVERSITY · PI WONG, WING H. · 2019 to 2022
$1.5M
Informatics resources for liquid biopsy researchU01CA237711 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LI, WENYUAN · 2019 to 2021
$1.1M
Comprehensive monitoring of diverse cancer outcomes using genomic and epigenomic sequencing of cfDNAK99CA290092 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Shuo Li · 2025 to 2026
$259k
NCI NIH HHS K99 CA290092NCI NIH HHS R01 CA246329NCI NIH HHS R01CA246329NCI NIH HHS R01 CA264864NCI NIH HHS U01 CA230705NCI NIH HHS U01CA230705NCI NIH HHS U01 CA237711NCI NIH HHS U01 CA285010NCI NIH HHS U01CA285010NCI NIH HHS U2C CA271898NHGRI NIH HHS R01 HG010359NHGRI NIH HHS R01HG010359
6 · The paper itself

Abstract

backgroundMachine learning models in biomedical research are often hindered by demographic imbalances in clinical datasets, leading to biased predictions that disadvantage minority populations. Existing bias-correction methods face limitations in handling the heterogeneity of biomedical data and the complexity of demographic influences.

resultsWe present DeBias, a computational framework for mitigating demographic biases in high-dimensional biomedical datasets. DeBias identifies and removes bias-associated subspaces from the feature space using control samples, enabling global correction of demographic distortions while preserving disease-specific signals. To evaluate its effectiveness, we apply DeBias to cell-free DNA methylation data for cancer detection. DeBias achieves a significant reduction in the number of features exhibiting demographic bias and outperforms existing methods in improving cancer detection performance for minority populations. Performance gains are validated in independent cohorts, highlighting the robustness of the approach.

conclusionsDeBias offers an effective and generalizable strategy for correcting demographic biases in biomedical machine learning. It represents a step toward more equitable machine learning models that can deliver reliable and unbiased predictions across diverse patient populations.

Indexed as

DNA MethylationMachine LearningNeoplasmsBiasDemographyHumansBias correctionCancer detectioncfDNA methylationDemographic biasMachine learning

Identifiers

PMID41736096
PMCPMC13104312

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LicenceCC BY
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Registered trials

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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.