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Updated: Aug 4, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Disease biomarker identification based on sample network optimization.

Pi-Jing Wei¹, Wenwen Ma², Yanxin Li³

¹Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, 230601 Hefei, Anhui, China.

Methods (San Diego, Calif.)

|March 31, 2023

Summary

This summary is machine-generated.

This study introduces a novel algorithm using sample similarity networks for disease biomarker identification. The method improves accuracy by considering sample associations, outperforming existing techniques.

Keywords:

Disease biomarker identification Multi-objective evolution algorithm Sample classification Sample similarity network

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Area of Science:

Biomedical informatics
Computational biology
Network analysis

Background:

Biomarkers are crucial for disease diagnosis, staging, and treatment.
Network-based methods are popular for biomarker identification but often neglect sample similarities.
Optimizing network construction is essential for accurate biomarker discovery.

Purpose of the Study:

To propose a multi-objective evolution algorithm for disease biomarker identification using sample similarity networks.
To enhance the accuracy of biomarker identification by incorporating sample structural information and associations.

Main Methods:

Designed sample similarity networks to capture structural characteristics and sample influence across classes.
Utilized an evolution algorithm with elite guidance and fusion selection strategies for iterative biomarker selection.
Classified samples based on the importance of selected biomarkers in each iteration.

Main Results:

The proposed algorithm demonstrated superior performance compared to state-of-the-art methods on five gene expression datasets.
The sample similarity networks effectively extracted relevant structural information among samples.
The elite guidance and fusion selection strategies improved the accuracy of sample classification.

Conclusions:

The developed multi-objective evolution algorithm offers a more effective strategy for disease biomarker identification.
Incorporating sample similarity networks enhances the precision of biomarker discovery in complex diseases.
This approach provides a foundation for developing improved diagnostic and therapeutic strategies.