基于深度学习的预测阿尔茨海默病使用微阵列基因表达数据
Mahmoud M Abdelwahab1,2, Khamis A Al-Karawi3,4, Hatem E Semary1,5
1Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia.
Biomedicines
|December 23, 2023
概括
深度学习模型与PCA和SVD等基因选择技术相结合,从微阵列数据中预测阿尔茨海默病 (AD) 的准确性很高,有助于早期诊断.
科学领域:
- 基因组学和生物信息学
- 计算神经科学是一种神经科学.
- 人工智能在医学中的应用
背景情况:
- 阿尔茨海默病 (AD) 是一种遗传复杂的神经退行性疾病.
- 微阵列技术提供了洞察力,但由于高维度和小样本大小而面临挑战.
- 基因选择对于改善AD的诊断模型至关重要.
研究的目的:
- 研究深度学习技术,特别是卷积神经网络 (CNN),用于使用基因表达数据预测阿尔茨海默病.
- 开发一个可靠的预测模型,用于早期AD检测和诊断.
- 为了提高神经退行性疾病的精准医学方法.
主要方法:
- 应用基因选择技术,包括主要组件分析 (PCA) 和奇点值分解 (SVD),以减少微阵列数据集的维度.
- 使用七层卷积神经网络 (CNN) 架构进行分类.
- 在阿尔茨海默病数据集上训练和评估PCA-CNN和SVD-CNN模型.
主要成果:
- 该PCA-CNN模型实现了96.60%的准确性,损失为0.3503.3%.
- 该SVD-CNN模型表现出卓越的性能,达到97.08%的准确性,损失为0.2466.
- 两种模型都强调了基因选择在提高分类准确性的有效性.
结论:
- 将基因选择方法与深度学习架构集成为改善阿尔茨海默病预测提供了强大的框架.
- 开发的模型显示了早期AD诊断和干预的巨大潜力.
- 未来的研究将探索替代基因选择技术和深度学习架构的更广泛的应用.
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