Guo Jinglan, Liao Jue, Chen Yuanlian, Wen Lisha, Cheng Song
Department of Medical Laboratory, Affiliated Hospital of Southwest Medical University, Lu Zhou, 646000, Si Chuan, China.
School of Basic Medical Sciences of Southwest Medical University, Lu Zhou, 646000, Si Chuan, China.
J Imaging Inform Med. 2025 Apr 1. doi: 10.1007/s10278-025-01492-9.
Microarray technology has become a vital tool in cardiovascular research, enabling the simultaneous analysis of thousands of gene expressions. This capability provides a robust foundation for heart disease classification and biomarker discovery. However, the high dimensionality, noise, and sparsity of microarray data present significant challenges for effective analysis. Gene selection, which aims to identify the most relevant subset of genes, is a crucial preprocessing step for improving classification accuracy, reducing computational complexity, and enhancing biological interpretability. Traditional gene selection methods often fall short in capturing complex, nonlinear interactions among genes, limiting their effectiveness in heart disease classification tasks. In this study, we propose a novel framework that leverages deep neural networks (DNNs) for optimizing gene selection and heart disease classification using microarray data. DNNs, known for their ability to model complex, nonlinear patterns, are integrated with feature selection techniques to address the challenges of high-dimensional data. The proposed method, DeepGeneNet (DGN), combines gene selection and DNN-based classification into a unified framework, ensuring robust performance and meaningful insights into the underlying biological mechanisms. Additionally, the framework incorporates hyperparameter optimization and innovative U-Net segmentation techniques to further enhance computational performance and classification accuracy. These optimizations enable DGN to deliver robust and scalable results, outperforming traditional methods in both predictive accuracy and interpretability. Experimental results demonstrate that the proposed approach significantly improves heart disease classification accuracy compared to other methods. By focusing on the interplay between gene selection and deep learning, this work advances the field of cardiovascular genomics, providing a scalable and interpretable framework for future applications.
微阵列技术已成为心血管研究中的一项重要工具，能够同时分析数千个基因的表达。这种能力为心脏病分类和生物标志物发现提供了坚实的基础。然而，微阵列数据的高维度、噪声和稀疏性给有效分析带来了重大挑战。基因选择旨在识别最相关的基因子集，是提高分类准确性、降低计算复杂度和增强生物学可解释性的关键预处理步骤。传统的基因选择方法在捕捉基因之间复杂的非线性相互作用方面往往存在不足，限制了它们在心脏病分类任务中的有效性。在本研究中，我们提出了一种新颖的框架，该框架利用深度神经网络（DNN）来优化基因选择并使用微阵列数据进行心脏病分类。DNN以其对复杂非线性模式进行建模的能力而闻名，它与特征选择技术相结合，以应对高维数据的挑战。所提出的方法DeepGeneNet（DGN）将基因选择和基于DNN的分类整合到一个统一的框架中，确保了强大的性能以及对潜在生物学机制的有意义洞察。此外，该框架还纳入了超参数优化和创新的U-Net分割技术，以进一步提高计算性能和分类准确性。这些优化使DGN能够提供强大且可扩展的结果，在预测准确性和可解释性方面均优于传统方法。实验结果表明，与其他方法相比，所提出的方法显著提高了心脏病分类的准确性。通过关注基因选择和深度学习之间的相互作用，这项工作推动了心血管基因组学领域的发展，为未来的应用提供了一个可扩展且可解释的框架。
