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BOOME: A Python package for handling misclassified disease and ultrahigh-dimensional error-prone gene expression data

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  • Li-Pang Chen

Abstract

In gene expression data analysis framework, ultrahigh dimensionality and measurement error are ubiquitous features. Therefore, it is crucial to correct measurement error effects and make variable selection when fitting a regression model. In this paper, we introduce a python package BOOME, which refers to BOOsting algorithm for Measurement Error in binary responses and ultrahigh-dimensional predictors. We primarily focus on logistic regression and probit models with responses, predictors, or both contaminated with measurement error. The BOOME aims to address measurement error effects, and employ boosting procedure to make variable selection and estimation.

Suggested Citation

  • Li-Pang Chen, 2022. "BOOME: A Python package for handling misclassified disease and ultrahigh-dimensional error-prone gene expression data," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-23, October.
    • Handle: RePEc:plo:pone00:0276664
    DOI: 10.1371/journal.pone.0276664
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    References listed on IDEAS

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    4. Li‐Pang Chen & Grace Y. Yi, 2021. "Analysis of noisy survival data with graphical proportional hazards measurement error models," Biometrics, The International Biometric Society, vol. 77(3), pages 956-969, September.
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    6. Li-Pang Chen & Grace Y. Yi, 2021. "Semiparametric methods for left-truncated and right-censored survival data with covariate measurement error," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(3), pages 481-517, June.
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