在多omics数据上的几何图形神经网络可以预测癌症生存结果
Jiening Zhu1, Jung Hun Oh2, Anish K Simhal2
1Department of Applied Mathematics & Statistics, Stony Brook University, NY, USA.
Computers in biology and medicine
|June 17, 2023
概括
我们开发了一种新的几何图形神经网络 (GGNN),用于分析复杂的癌症基因组数据. 该方法通过将生物网络特征与多omics数据集成来改善患者预后预测.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 机器学习是机器学习.
背景情况:
- 先进的测序使得全面的癌症基因组表征成为可能.
- 高维基因组数据需要复杂的计算方法来预测和治疗.
- 传统的机器学习与基因组数据集的高维,低样本大小的性质作斗争.
研究的目的:
- 开发一种先进的计算方法来分析癌症多omics数据.
- 解决传统机器学习在处理高维基因组数据方面的局限性.
- 通过使用几何网络分析和深度学习来改善患者的预后预测和治疗策略.
主要方法:
- 开发了一种新的监督深度学习方法:几何图形神经网络 (GGNN).
- 从生物网络 (PPI,途径) 整合的几何特征 (例如,奥利维埃-里奇曲率) 进入深度学习.
- 利用基于已知的生物网络信息的稀疏连接的图形神经网络.
- 在网络层中集成几何特征和多omics数据.
- 在特征选择和多变量考克斯比例危险回归建模中采用了局部-全球原则.
主要成果:
- 与替代方法相比,GGNN方法的预测性能优于其他方法.
- 来自基因组网络的几何特征被证明可以预测各种癌症的生存结果.
- 该方法成功地应用于来自CoMMpass研究和癌症基因组图谱 (TCGA) 的多omics数据.
结论:
- 拟议的GGNN方法提高了癌症多omics数据分析的预测能力和可解释性.
- 将几何网络特征与深度学习相结合,为改善癌症预后提供了一个有前途的方法.
- 这项研究强调了先进的计算技术在精密瘤学的潜力.
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