Evidence map›Paper›PMID 42146637›Full record

ArticlebioRxiv : the preprint server for biology2026

Machine learning cross-platform proteomic imputation enables protein quality scoring and replication of epidemiological associations.

Linke Li, Ahmed Alaa, Youxin Tan, Ilker Demirel, Samuel Friedman, Qiayi Zha, Russell Tracy, Kent D Taylor, Bing Yu, Christie M Ballantyne and 20 more

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Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

30 authors.

Linke LiClinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA.ORCID 0009-0002-6350-5121
Ahmed AlaaDepartment of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA.
Youxin TanProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA.
Ilker DemirelMassachusetts Institute of Technology, Cambridge, MA.
Samuel FriedmanML4H, Broad Institute of Harvard and MIT, Cambridge, MA.
Qiayi ZhaProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA.
Russell TracyDepartment of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT.
Kent D TaylorThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA.
Bing YuDepartment of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX.
Christie M BallantyneDepartment of Medicine, Baylor College of Medicine, Houston, TX.
Rajat DeoDivision of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.
Ruth DubinDepartment of Medicine, University of Texas Southwestern Medical Center, Dallas, TX.
Michael Y TsaiDepartment of Laboratory Medicine & Pathology, University of Minnesota Medical School, Minneapolis, MN.
Gina M PelosoDepartment of Biostatistics, Boston University School of Public Health, Boston, MA.
Jennifer BrodyCardiovascular Health Research Unit, Department of Medicine & Deparment of Epidemiology, University of Washington, Seattle, WA.
Tom AustinCardiovascular Health Research Unit, Department of Medicine & Deparment of Epidemiology, University of Washington, Seattle, WA.
Bruce M PsatyCardiovascular Health Research Unit, Department of Medicine & Deparment of Epidemiology, University of Washington, Seattle, WA.
Jayna NicholasDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Laura M RaffieldDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Usman TahirDivision of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA.
Josef CoreshDepartment of Medicine, NYU Grossman School of Medicine, New York, NY, USA.
Whitney HornsbyProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA.
Andrew ChanClinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA.
Stephen S RichDepartment of Genome Sciences, University of Virginia, Charlottesville, VA.
Jerome I RotterThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA.
Peter GanzDivision of Cardiology, Zuckerberg San Francisco General Hospital and Department of Medicine, University of California San Francisco, San Francisco, CA.
Robert GersztenHeart and Vascular Institute, Mass General Brigham, Boston, MA.
Anthony PhilippakisGoogle Ventures, Cambridge, MA.
Pradeep NatarajanProgram in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA.
Zhi YuClinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA.

Funding

UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1M
Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
Clinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2M
Task Area A Core Study Operations.Task Area A shall encompass annual follow-up of cohort members, clinical endpoints ascertainment, study coordination activities, maintenance of the database and biosp75N92020D00001 · NHLBI · UNIVERSITY OF WASHINGTON · PI MCCLELLAND, ROBYN LEAGH · 2020 to 2025
$17.2M
Studies of Rare Genetic Variation in the Isolated Population of SardiniaR01HL117626 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABECASIS, GONCALO · 2013 to 2016
$10.5M
Exceptional Survival: Trajectories to Functional Aging (CHS All Stars)R01AG023629 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI NEWMAN, ANNE B. · 2004 to 2016
$9.5M
CHARGE Consortium: Omics Discovery for CVD and Aging PhenotypesR01HL105756 · NHLBI · UNIVERSITY OF WASHINGTON · PI Bruce M Psaty, NICHOLAS L SMITH · 2011 to 2026
$9.5M
Rare variants and NHLBI traits in deeply phenotyped cohortsR01HL120393 · NHLBI · UNIVERSITY OF WASHINGTON · PI PSATY, BRUCE M, RICE, KENNETH M. · 2014 to 2016
$8.9M
CARDIOVASCULAR HEALTH STUDY (CHS) - TASK AREA C, STUDY CLOSEOUT75N92021D00006 · NHLBI · UNIVERSITY OF WASHINGTON · PI PSATY, BRUCE · 2021 to 2024
$8.4M
CHS research resources for the cardiovascular health of older adultsU01HL130114 · NHLBI · UNIVERSITY OF WASHINGTON · PI BURKE, GREGORY L, KRONMAL, RICHARD A · 2016 to 2019
$6.6M
Plasma proteomics in CHS and population biologyR01HL144483 · NHLBI · UNIVERSITY OF WASHINGTON · PI DURDA, JON PETER, GERSZTEN, ROBERT E · 2019 to 2022
$6.0M
NCATS NIH HHS UL1 TR000040NCATS NIH HHS UL1 TR001079NCATS NIH HHS UL1 TR001420NCATS NIH HHS UL1 TR001881NHGRI NIH HHS R00 HG012956NHLBI NIH HHS 75N92020D00001NHLBI NIH HHS 75N92020D00002NHLBI NIH HHS 75N92020D00003NHLBI NIH HHS 75N92020D00004NHLBI NIH HHS 75N92020D00005NHLBI NIH HHS 75N92020D00006NHLBI NIH HHS 75N92020D00007NHLBI NIH HHS 75N92021D00006NHLBI NIH HHS HHSN268200800007CNHLBI NIH HHS HHSN268201200036CNHLBI NIH HHS HHSN268201500003CNHLBI NIH HHS HHSN268201500003INHLBI NIH HHS HHSN268201800001CNHLBI NIH HHS N01 HC055222NHLBI NIH HHS N01 HC085079NHLBI NIH HHS N01 HC085080NHLBI NIH HHS N01 HC085081NHLBI NIH HHS N01 HC085082NHLBI NIH HHS N01 HC085083NHLBI NIH HHS N01 HC085086NHLBI NIH HHS N01 HC095159NHLBI NIH HHS N01 HC095160NHLBI NIH HHS N01 HC095161NHLBI NIH HHS N01 HC095162NHLBI NIH HHS N01 HC095163NHLBI NIH HHS N01 HC095164NHLBI NIH HHS N01 HC095165NHLBI NIH HHS N01 HC095166NHLBI NIH HHS N01 HC095167NHLBI NIH HHS N01 HC095168NHLBI NIH HHS N01 HC095169NHLBI NIH HHS R01 HL087652NHLBI NIH HHS R01 HL103612NHLBI NIH HHS R01 HL105756NHLBI NIH HHS R01 HL117626NHLBI NIH HHS R01 HL120393NHLBI NIH HHS R01 HL142711NHLBI NIH HHS R01 HL144483NHLBI NIH HHS R01 HL172803NHLBI NIH HHS U01 HL080295NHLBI NIH HHS U01 HL120393NHLBI NIH HHS U01 HL130114NIA NIH HHS R01 AG023629NIEHS NIH HHS 75N98025D00022NIEHS NIH HHS 75N98025D00024NIEHS NIH HHS 75N98025D00025NIEHS NIH HHS 75N98025D00026NIEHS NIH HHS 75N98025D00027NIEHS NIH HHS 75N98025D00028
6 · The paper itself

Abstract

High-throughput affinity-based proteomics has advanced biomedical research, yet fundamental, persistent discordance between mainstream platforms (SomaScan and Olink) routinely undermines the replication of findings. This platform-driven non-replication complicates downstream biological validation and biomarker prioritization. Here, we develop a machine learning-based framework for cross-platform protein value imputation to resolve this translational bottleneck. Using paired proteomic data measured by both SomaScan and Olink from 5,325 participants of the Multi-Ethnic Study of Atherosclerosis, we developed models to impute cross-platform measurements and applied them to two independent and demographically distinct cohorts (Cardiovascular Health Study [N=3,171] and UK Biobank [UKB; N=41,405]) for external validation. Our bi-directional model 1) established an imputation performance-based protein fidelity index, validated against gold-standard measurements from Atherosclerosis Risk in Communities study (N=101) and Nurses' Health Study (N=54), 2) enabled imputation of platform-exclusive protein measurements, and 3) facilitated calibration of overlapping proteins. We demonstrate the utility of this framework through three applications: 1) fidelity-informed analyses enhanced the replication of biomarker discovery, 2) recovery of SomaScan signals that were previously inaccessible in UKB's original Olink measurements, and 3) improved replication performance for overlapping proteins. Our study offers a translational roadmap that allows researchers to achieve reliable epidemiological replication, target specific assays for future optimization, and prioritize biological signal over platform noise.

Identifiers

PMID42146637
PMCPMC13174605

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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.