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

Area of Science:

• Bioinformatics
• Statistical Genetics
• Computational Biology

Background:

• Gene expression data analysis often involves ultrahigh dimensionality and measurement errors.
• Accurate regression modeling requires addressing these challenges through error correction and variable selection.

Purpose of the Study:

• Introduce the Python package BOOME (BOOsting algorithm for Measurement Error).
• Address measurement error effects in logistic and probit models.
• Perform variable selection and estimation in ultrahigh-dimensional settings.

Main Methods:

• Utilizes a boosting procedure for variable selection and estimation.
• Focuses on logistic and probit regression models.
• Handles measurement error in responses, predictors, or both.

Main Results:

• The BOOME package provides a computational framework for handling complex gene expression data.
• Demonstrates the capability to correct for measurement error effects.
• Enables effective variable selection in ultrahigh-dimensional scenarios.

Conclusions:

• BOOME offers a robust solution for gene expression data analysis with measurement error.
• Facilitates more accurate regression modeling and variable selection.
• A valuable tool for researchers in bioinformatics and related fields.