Evidence map›Paper›PMID 40064867›Full record

ArticleNature communications2025

Equitable machine learning counteracts ancestral bias in precision medicine.

Leslie A Smith, James A Cahill, Ji-Hyun Lee, Kiley Graim

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Leslie A SmithDepartment of Computer & Information Science & Engineering, University of Florida, 1889 Museum Rd, Gainesville, 32611, FL, USA.
James A CahillEnvironmental Engineering Sciences Department, University of Florida, 365 Weil Hall, Gainesville, 32611, FL, USA.
Ji-Hyun LeeDepartment of Biostatistics, University of Florida, 2004 Mowry Rd, Gainesville, Gainesville, 32603, FL, USA.ORCID http://orcid.org/0000-0001-6420-5150
Kiley GraimDepartment of Computer & Information Science & Engineering, University of Florida, 1889 Museum Rd, Gainesville, 32611, FL, USA. kgraim@ufl.edu.ORCID http://orcid.org/0000-0002-4569-8444

Funding

The DARC side of Breast Cancer Disparities - African Ancestry and Cancer- Related Immune ResponseR01CA259396 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI DAVIS, MELISSA B, YATES, CLAYTON · 2021 to 2025
$2.4M
Leveraging Mammalian Cancers, Platinum-Quality Genome Assemblies, and Large-Scale Data to Identify Mechanisms of Rare Human CancersR01CA265907 · NCI · UNIVERSITY OF FLORIDA · PI Kiley Graim · 2022 to 2026
$1.6M
NCI NIH HHS R01 CA259396NCI NIH HHS R01 CA265907U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) 5R01CA265907
6 · The paper itself

Abstract

Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease. Therapeutics and outcomes remain hidden because we lack insights that could be gained from analyzing ancestrally diverse genomic data. To address this significant gap, we present PhyloFrame, a machine learning method for equitable genomic precision medicine. PhyloFrame corrects for ancestral bias by integrating functional interaction networks and population genomics data with transcriptomic training data. Application of PhyloFrame to breast, thyroid, and uterine cancers shows marked improvements in predictive power across all ancestries, less model overfitting, and a higher likelihood of identifying known cancer-related genes. Validation in fourteen ancestrally diverse datasets demonstrates that PhyloFrame is better able to adjust for ancestry bias across all populations. The ability to provide accurate predictions for underrepresented groups, in particular, is substantially increased. Analysis of performance in the most diverse continental ancestry group, African, illustrates how phylogenetic distance from training data negatively impacts model performance, as well as PhyloFrame's capacity to mitigate these effects. These results demonstrate how equitable artificial intelligence (AI) approaches can mitigate ancestral bias in training data and contribute to equitable representation in medical research.

Indexed as

Machine LearningPrecision MedicineBreast NeoplasmsFemaleGenomicsHumansNeoplasmsPhylogeny

Identifiers

PMID40064867
PMCPMC11894161

What OpenQuestion holds

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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.