拉曼光谱与卷积神经网络相结合,用于乳腺癌亚型分类和关键特征可视化
Juan Li1, Xiaoting Wang1, Shungeng Min2
1School of Pharmaceutical Sciences and Institute of Materia Medica & Xinjiang Key Laboratory of Biological Resources and Genetic Engineering, College of Life Science and Technology, Xinjiang University, Urumqi, 830017, China.
Computer methods and programs in biomedicine
|August 8, 2024
概括
使用卷积神经网络 (CNN) 的拉曼光谱学可以准确识别乳腺癌亚型. 可视化技术精确地确定了关键的光谱生物标志物,有助于亚型差异化和潜在的生物标志物发现.
科学领域:
- 生物医学光谱学 生物医学光谱学
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 拉曼光谱可提供非侵入性乳腺癌分析,但目前的模型缺乏全面的亚型覆盖和可视化.
- 使用拉曼光谱的乳腺癌分子亚型的现有预测模型在范围和解释性上是有限的.
研究的目的:
- 开发一种基于拉曼光谱的预测模型,使用CNN用于乳腺癌分子亚型.
- 通过可视化策略来识别重要的光谱峰值,用于生物标记物挖掘.
- 为了提高乳腺癌亚型的模型性能和可解释性.
主要方法:
- 卷积神经网络 (CNN) 与拉曼光谱学集成,用于乳腺癌亚型.
- 搜索算法 (SSA) 优化了CNN参数,以提高预测准确度.
- 蒙特卡洛采样确保了结果的可靠性;梯度加权类激活映射 (Grad-CAM) 可视化了关键的光谱区域.
主要成果:
- 与其他算法相比,优化的CNN实现了更高的准确性和更低的标准误差.
- 光谱指纹区域对于分类是最关键的,与全光谱的性能差异最小.
- 在乳腺癌亚型分类方面,CNN模型获得了95.34%±2.18%的准确性,表现优于SVM,PLS-DA和KNN.
结论:
- 拉曼光谱与CNN相结合,能够准确快速地识别乳腺癌分子亚型.
- 拟议的可视化策略与生化和空间信息保持一致,支持生物标志物挖掘.
- 这种方法有望促进乳腺癌诊断和生物标志物发现.
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