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嵌入PaCMAP的卷积神经网络用于多omics数据集成
Hazem Qattous1, Mohammad Azzeh2, Rahmeh Ibrahim3
1Software Engineering Department, Princess Sumaya University for Technology, Amman P.O. Box 1438, Jordan.
Heliyon
|January 1, 2024
概括
这项研究整合了用于癌症预后的多omics数据,在使用新的CNN模型预测格里森分数时达到98.89%的准确性. 这一进步提高了癌症诊断和个性化医学的精度.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 多领域数据集成对于推进医疗保健中的预测模型至关重要.
- 癌症诊断的准确性对于有效的临床决策至关重要.
- 目前的预后方法需要改进,以提高准确性.
研究的目的:
- 开发一种创新的方法来整合拷贝数改变 (CNA),DNA甲基化和基因表达数据.
- 为了提高癌症诊断的预后准确性.
- 为了提高前列腺癌患者的临床决策.
主要方法:
- 利用PaCMAP进行维度减小,将三个omics数据合并为一个2D地图.
- 使用RGB配色方案可视化集成的多omics数据.
- 将卷积神经网络 (CNN) 应用于格里森得分预测的2D地图.
主要成果:
- 拟议的模型实现了98.89%的预测准确度.
- 该模型显示曲线下的高面积 (AUC) 为0.9996.6.
- 在预测格里森分数方面表现优于现有的i-SOM-GSN模型.
结论:
- 多omics数据集成显著提高健康结果的预测.
- PaCMAP,RGB可视化和CNN的综合方法为异质的omics数据集成提供了一个强大的框架.
- 这些发现支持个性化医学的进步和前列腺癌的临床决策支持.
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