MPBART - Multinomial Probit Bayesian Additive Regression Trees

Kindo, Bereket P.; Wang, Hao; Peña, Edsel A.

doi:10.48550/arxiv.1309.7821

Cited by 2 publications

(2 citation statements)

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“…A similar strategy could be considered for the IntegratedLearner base-learners during prior elicitation, wherein, a literature-curated tensor can be used to further refine the training and validation process. Although we have only considered the setup of continuous and binary outcomes, the extension to count or survival outcome prediction and multi-label classification is immediate 22,23,24 .…”

Section: Discussionmentioning

confidence: 99%

An integrated Bayesian framework for multi-omics prediction and classification

Mallick

Porwal

Saha³

et al. 2022

Preprint

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With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to the more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by borrowing information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner

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Section: Discussionmentioning

confidence: 99%

An integrated Bayesian framework for multi-omics prediction and classification

Mallick

Porwal

Saha³

et al. 2022

Preprint

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“…BART can easily be modified to handle classification problems for categorical response variables. In , only binary outcomes were explored but recent work has extended BART to the multiclass problem (Kindo, Wang, and Pe 2013). Our implementation handles binary classification and we plan to implement multiclass outcomes in a future release.…”

Section: Bart For Classificationmentioning

confidence: 99%

bartMachine: Machine Learning with Bayesian Additive Regression Trees

Kapelner¹,

Bleich²

2016

J. Stat. Soft.

237

288

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We present a new package in R implementing Bayesian additive regression trees (BART). The package introduces many new features for data analysis using BART such as variable selection, interaction detection, model diagnostic plots, incorporation of missing data and the ability to save trees for future prediction. It is significantly faster than the current R implementation, parallelized, and capable of handling both large sample sizes and high-dimensional data.

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MPBART - Multinomial Probit Bayesian Additive Regression Trees

Cited by 2 publications

References 0 publications

An integrated Bayesian framework for multi-omics prediction and classification

An integrated Bayesian framework for multi-omics prediction and classification

bartMachine: Machine Learning with Bayesian Additive Regression Trees

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