Evidence map›Paper›PMID 41751941›Full record

ArticleInternational journal of molecular sciences2026

Interpretable Machine Learning with SHAP Identifies Key Biomarkers in a Multi-Factorial Spectrum of Age-Related Neurological and Metabolic Conditions.

Daniil V Artamonov, Polina I Popova, Ekaterina A Korf, Natalia G Voitenko, Alisa A Chernysheva, Pavel V Avdonin, Richard O Jenkins, Nikolay V Goncharov

Abstract read
In one paragraph

Article in International journal of molecular sciences, 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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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Daniil V ArtamonovGroup of Theoretical Chemistry, N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, Leninsky Prospect 47, Moscow 119991, Russia.ORCID 0009-0007-0858-8098
Polina I PopovaSt. Petersburg State Budgetary Healthcare Institution "City Polyclinic No. 107", St. Petersburg 195030, Russia.
Ekaterina A KorfSechenov Institute of Evolutionary Physiology and Biochemistry, Russian Academy of Sciences, pr. Torez 44, St. Petersburg 194223, Russia.
Natalia G VoitenkoSechenov Institute of Evolutionary Physiology and Biochemistry, Russian Academy of Sciences, pr. Torez 44, St. Petersburg 194223, Russia.ORCID 0000-0002-3164-4971
Alisa A ChernyshevaSechenov Institute of Evolutionary Physiology and Biochemistry, Russian Academy of Sciences, pr. Torez 44, St. Petersburg 194223, Russia.
Pavel V AvdoninKoltsov Institute of Developmental Biology, Russian Academy of Sciences, 26 Vavilov St., Moscow 119334, Russia.ORCID 0000-0002-4138-1589
Richard O JenkinsLeicester School of Allied Health Sciences, De Montfort University, The Gateway, Leicester LE1 9BH, UK.
Nikolay V GoncharovSechenov Institute of Evolutionary Physiology and Biochemistry, Russian Academy of Sciences, pr. Torez 44, St. Petersburg 194223, Russia.

Funding

Russian Science Foundation 22-15-00155-П
6 · The paper itself

Abstract

Vascular and metabolic disorders in the elderly-including acute ischemic stroke (AIS), chronic cerebral circulation insufficiency (CCCI), type 2 diabetes mellitus (DM), and subcortical ischemic vascular dementia (SIVD)-pose a major diagnostic challenge due to their reliance on multi-parameter blood chemistry. In this study, 49 biochemical features were analyzed within a cohort of 120 patients. The application of variance-aware statistical testing revealed that several features (e.g., Fe, Transf, RDW%, LDL) exhibited statistically significant heterogeneity of variance (

Indexed as

AgingBiomarkersMachine LearningMetabolic DiseasesAgedBoosting Machine Learning AlgorithmsClassification AlgorithmsClustering AlgorithmsDementia, VascularDiabetes Mellitus, Type 2FemaleHumansBiomarkersage-related diseasesbiomarker discoveryclassificationfeature selectionheteroscedasticitymachine learningSHAP (Shapley Additive exPlanations)type 2 diabetesvascular dementiaWelch’s ANOVA

Identifiers

PMID41751941
PMCPMC12941188

What Socratic holds

Textmetadata
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 Socratic graph.