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

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 ref...

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Bibliographic Details
Published in:PloS one 2022-10, Vol.17 (10), p.e0276664-e0276664
Main Author: Chen, Li-Pang
Format: Article
Language:English
Subjects:
Algorithms
Biology and Life Sciences
Bone marrow
Computer and Information Sciences
Data analysis
Datasets
Diagnosis
Diagnostic errors
Engineering and Technology
Error analysis
Error correction
Feature selection
Gene expression
Generalized linear models
Genetic aspects
Health aspects
Leukemia
Medicine and Health Sciences
Methods
Microscopy
Physical Sciences
Prevention
Regression analysis
Regression models
Research and Analysis Methods
Risk factors
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0276664