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New method attributes disease effects in age biomarkers to training data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called pyinfluence to attribute the disease-related effects in normative age biomarkers to individual training samples. This technique, validated against leave-one-out retraining, ranks training samples by their influence on held-out case-control separation. When applied to UK Biobank data across four diseases, removing the top 10% most influential training samples consistently increased the held-out disease-related effect size, notably doubling the metabolomic-age effect for type-2 diabetes and raising the brain-age effect for multiple sclerosis. AI

IMPACT This research could lead to more robust and interpretable age-prediction models, improving disease risk assessment by identifying key training data points.

RANK_REASON The cluster contains a research paper detailing a new methodology for attributing effects in machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method attributes disease effects in age biomarkers to training data

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The cluster contains a research paper detailing a new methodology for attributing effects in machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jakob Snel, Marc-Andre Schulz ·

    Attributing Cohen's d: Training Data Attribution for Disease-Related Effects in Normative Age Biomarkers

    arXiv:2609.07729v1 Announce Type: new Abstract: Normative age models are trained to predict chronological age in a nominally healthy cohort. Applied to patients, they deviate, and the gap between predicted and chronological age is read as disease risk. Here, we attribute the dise…

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