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人工智能驱动的深度学习方法用于全癌症免疫分析
Minh Huu Nhat Le1,2,3, Ha-Hieu Pham4, Huy Quoc Nguyen5
1International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Studies in health technology and informatics
|August 8, 2025
概括
这项研究使用RNA-Seq数据和卷积神经网络 (CNN) 来分类瘤免疫微环境 (TME) 亚型. 在CNN模型准确地识别免疫亚型,帮助癌症研究和治疗策略.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 瘤免疫微环境 (TME) 对癌症的进展和治疗反应至关重要.
- RNA测序 (RNA-Seq) 使得在TME中识别出不同的免疫亚型成为可能.
- 这些亚型包括伤口愈合 (WH),IFN主导 (IFNG),炎症性 (INF),淋巴细胞枯竭 (LD),免疫性安静 (IQ) 和TGF-β主导 (TGFb).
研究的目的:
- 开发和评估一个卷积神经网络 (CNN) 模型,用于分类已知的六种RNA-Seq定义的TME免疫亚型.
- 评估CNN模型在处理阶级不平衡和捕捉复杂基因表达相互作用方面的表现.
主要方法:
- 利用RNA-Seq数据来训练一个卷积神经网络 (CNN) 模型.
- 美国有线电视新闻网的架构包含了ReLU激活和停机功能,以提高性能.
- 使用十倍交叉验证评估模型性能,测量F1得分和曲线下的面积 (AUC).
- 与其他机器学习模型比较CNN的性能,包括XGBoost,随机森林和TabNet.
主要成果:
- 在CNN模型中,F1得分高达0.9483的10倍,AUC为0.9969.
- 与XGBoost,随机森林和TabNet.Net相比,在分类TME免疫亚型方面表现出卓越的表现.
- 证实了CNN有效管理阶级不平衡和模拟复杂的基因相互作用模式的能力.
结论:
- 卷积神经网络非常有效地根据RNA-Seq数据对瘤免疫微环境亚型进行分类.
- 开发的CNN模型为癌症研究中的免疫分析提供了一个强大的工具.
- 未来的研究将专注于在独立数据集上验证这些发现,并整合多学科数据以提高准确性.
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