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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Deep centroid: a general deep cascade classifier for biomedical omics data classification.

Kuan Xie¹, Yuying Hou¹, Xionghui Zhou^1,2

¹Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, People's Republic of China.

Bioinformatics (Oxford, England)

|February 2, 2024

Summary

A new Deep Centroid classifier improves biomedical omics data classification for precision medicine. This novel method outperforms traditional models in cancer diagnosis, prognosis, and drug sensitivity prediction.

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Area of Science:

Biomedical data science
Machine learning in biology
Precision medicine

Background:

Biomedical omics data classification is crucial but faces challenges like high dimensionality and small sample sizes.
Traditional machine learning models struggle with these characteristics, especially on independent datasets.

Purpose of the Study:

To develop a novel classifier, Deep Centroid, that addresses the limitations of traditional models in biomedical omics data classification.
To evaluate Deep Centroid's performance in precision medicine applications.

Main Methods:

Deep Centroid is an ensemble learning method with a multi-layer cascade structure, incorporating feature scanning and cascade learning.
The classifier combines the stability of nearest centroid methods with deep learning strategies.
Applied to cancer early diagnosis, cancer prognosis, and drug sensitivity prediction using diverse omics data.

Main Results:

Deep Centroid significantly outperformed six traditional machine learning models across all three precision medicine applications.
The features identified by Deep Centroid demonstrated biological significance, indicating model interpretability.
The classifier showed robust performance in classifying biomedical omics data.

Conclusions:

Deep Centroid offers a promising approach for accurate and interpretable classification of biomedical omics data.
The method has strong potential for advancing precision medicine applications.
The developed classifier is available for public use.