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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Overlapping group screening for binary cancer classification with TCGA high-dimensional genomic data.

Jie-Huei Wang¹, Yi-Hau Chen²

¹Department of Statistics, Feng Chia University, Taichung 40724, Taiwan.

Journal of Bioinformatics and Computational Biology

|June 23, 2023

Summary

This summary is machine-generated.

This study introduces an overlapping group screening (OGS) method for accurate cancer diagnosis using gene expression data. The OGS approach improves prediction accuracy by integrating gene pathway information, outperforming existing machine learning techniques.

Keywords:

Cancer diagnosis TCGA gene–gene interaction logistic regression overlapping group screening precision medicine

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Area of Science:

Computational Biology
Genomics
Precision Medicine

Background:

Cancer diagnosis is crucial for effective treatment and patient survival in precision medicine.
Genomic data, particularly microarray gene expression profiling, offers potential for cancer classification.
High-dimensional feature spaces and feature contamination pose challenges in genomic diagnostic model development.

Purpose of the Study:

To develop an accurate cancer diagnosis model using an overlapping group screening (OGS) approach.
To predict patient probability within disease classification categories using logistic regression.
To integrate gene pathway information for identifying key genes and interactions in cancer classification.

Main Methods:

Proposed an overlapping group screening (OGS) method within a logistic regression framework.
Integrated gene pathway information to identify relevant genes and gene-gene interactions.
Conducted simulation studies and applied the method to The Cancer Genome Atlas (TCGA) data for lung adenocarcinoma, liver hepatocellular carcinoma, and thyroid carcinoma.

Main Results:

The proposed OGS method demonstrated superior predictive accuracy compared to existing machine learning methods in simulation studies.
Accurate cancer diagnosis models were successfully established for lung adenocarcinoma, liver hepatocellular carcinoma, and thyroid carcinoma using TCGA genomic data.

Conclusions:

The OGS approach effectively addresses challenges in high-dimensional genomic data for cancer diagnosis.
Integrating gene pathway information enhances the identification of biologically relevant features for improved diagnostic model performance.
This method offers a promising tool for advancing precision medicine through accurate cancer classification.