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Related Experiment Video

Updated: Jun 20, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

Joint variable selection and classification with immunohistochemical data.

Debashis Ghosh¹, Ratna Chakrabarti

¹Departments of Statistics and Public Health Sciences, Pennsylvania State University, 514A Wartik Lab, University Park, PA 16802.

Biomarker Insights

|August 18, 2009

Summary

This summary is machine-generated.

This study introduces machine learning for analyzing cancer biomarker data, improving feature selection and classification for clinical utility. Novel methods enhance the analysis of complex immunohistochemical staining profiles.

Keywords:

L1 penalty LASSO algorithm antibody protein Ghosh and Chakrabarti tissue microarray

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Last Updated: Jun 20, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

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Published on: April 8, 2016

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Biomedical data analysis
Computational pathology
Machine learning in oncology

Background:

Immunohistochemistry is crucial for validating cancer biomarkers.
Traditional analyses often overlook the multivariate nature of staining profiles.
Integrating machine learning offers new analytical possibilities.

Purpose of the Study:

To apply machine learning to immunohistochemical data for biomarker validation.
To address the limitations of current analytical methods for complex staining patterns.
To jointly optimize feature selection and classification in cancer biomarker studies.

Main Methods:

Utilized the least absolute selection and shrinkage operator (LASSO) for estimation.
Developed novel models and algorithms for compositional data analysis.
Applied machine learning techniques to multivariate immunohistochemical staining profiles.

Main Results:

Demonstrated effective feature selection and classification using the proposed methods.
Introduced flexible models for analyzing complex immunohistochemical data.
Successfully applied the techniques to a real-world cancer biomarker study.

Conclusions:

The developed machine learning approach enhances the analysis of immunohistochemical data.
This method improves the clinical utility assessment of candidate cancer biomarkers.
Novel algorithms offer a more robust way to analyze compositional biomarker data.