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New spBART model enhances risk prediction with high-dimensional epigenetic data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new semi-parametric Bayesian Additive Regression Trees (spBART) model to improve risk prediction using high-dimensional epigenetic data alongside lower-dimensional covariates. This method separates the modeling of low-dimensional covariates into a parametric component for interpretability and uses tree ensembles for complex, high-dimensional predictors. Applied to multiple myeloma studies, the spBART model successfully identified key genetic loci and achieved a strong out-of-sample discrimination AUC of 0.96. AI

IMPACT Introduces a novel statistical framework for integrating complex biological data, potentially advancing precision medicine and risk assessment in disease.

RANK_REASON The cluster contains a new academic paper detailing a novel statistical model for risk prediction.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New spBART model enhances risk prediction with high-dimensional epigenetic data

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The cluster contains a new academic paper detailing a novel statistical model for risk prediction.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Saurabh Bhandari, Brian C. -H. Chiu, Parveen Bhatti, Yuan Ji ·

    Semi-Parametric Bayesian Additive Regression Trees for Risk Prediction with High-Dimensional Epigenetic Signatures and Low-Dimensional Covariates

    arXiv:2605.20143v1 Announce Type: cross Abstract: In the era of precision medicine, genome-wide epigenetic modifications offer rich data that could inform risk prediction. However, these data are high-dimensional and exhibit complex dependence structures, which makes it difficult…

  2. arXiv stat.ML TIER_1 English(EN) · Yuan Ji ·

    Semi-Parametric Bayesian Additive Regression Trees for Risk Prediction with High-Dimensional Epigenetic Signatures and Low-Dimensional Covariates

    In the era of precision medicine, genome-wide epigenetic modifications offer rich data that could inform risk prediction. However, these data are high-dimensional and exhibit complex dependence structures, which makes it difficult to jointly model them with low-dimensional covari…

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