Development of Plasma Protein Classification Models for Alzheimer's Disease Using Multiple Machine Learning Approaches.

Amy Tsurumi, Catherine M Cahill, Andy J Liu, Pranam Chatterjee, Sudeshna Das, Ami Kobayashi

Abstract read

In one paragraph

Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

Fast activity prediction of chemically modified siRNAs via structure-based energy calculations and inference-augmented tabular deep learning.Molecular therapy. Nucleic acids · 2026
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5 · Who and what money

Authors and funding

6 authors.

Amy TsurumiDepartment of Surgery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St., Boston, MA 02114, USA.ORCID 0000-0001-7678-169X

Catherine M CahillNeurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, 149 13th St., Charlestown, MA 02129, USA.

Andy J LiuDepartment of Neurology, Duke University School of Medicine, 3116 N. Duke St., Durham, NC 27704, USA.ORCID 0000-0003-3096-1788

Pranam ChatterjeeDepartment of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, USA.

Sudeshna DasDepartment of Neurology, Massachusetts General Hospital and Harvard Medical School, 65 Lansdowne St., Cambridge, MA 02139, USA.

Ami KobayashiDepartment of Neurology, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Rd., Boston, MA 02115, USA.ORCID 0000-0001-7347-7537

Funding

Alzheimer's Association AARG-NTF-1244080Meiji Yasuda Life Foundation of Health and Welfare Health Science Research Grant

6 · The paper itself

Abstract

Alzheimer's Disease (AD) management is challenging due to limitations in detection methods. Currently, cerebrospinal fluid (CSF) biomarkers involve assessing β-amyloid (Aβ) and phosphorylated tau proteins. The lumbar puncture procedure to obtain CSF is invasive and sometimes causes significant anxiety in patients. In contrast, plasma biomarkers would allow rapid, accurate, and cost-effective diagnosis, while minimizing invasiveness and discomfort. Using a dataset involving 120 plasma proteins from clinically diagnosed AD patients versus cognitively normal subjects, we developed classification models by applying various machine learning algorithms (EBlasso, EBEN, XGBoost, LightGBM, TabNet, and TabPFN) to plasma proteomic measurements. Gene ontology and pathway enrichment, and a literature review were used to evaluate the potential relevance of the biomarkers identified in AD-related mechanisms. Biomarkers identified were also evaluated for the enrichment of aging-related biomarkers. The models developed yielded high AUROC and accuracy, mostly >0.9. Proteins selected as predictors by all the models included Angiopoietin-2 (ANG-2), epidermal growth factor (EGF), Interleukin 1α (IL-1α), and platelet growth factor subunit B (PDGF-BB). Ample previous literature supported their relevance in AD. The pool of all the biomarkers identified was significantly enriched with known aging-related biomarkers (

Indexed as

Alzheimer DiseaseBlood ProteinsMachine LearningAgedBiomarkersFemaleHumansMaleProteomicsBiomarkersBlood ProteinsagingAlzheimer’s diseasebiomarkersdiagnosismachine learningneurodegenerationproteomics

Identifiers

PMID41373821

DOI10.3390/ijms262311673

PMCPMC12692058

What Socratic holds

Textmetadata

LicenceCC BY

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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 Socratic graph.