Work

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

Year: 2022

Type: software-paper

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.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0276664

Author: Li-Pang Chen

Institution: National Chengchi University

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $1,805

Funder: Hsinchu Science Park Bureau, Ministry of Science and Technology, Taiwan