一个用于卵巢癌的预后预测模型,使用交叉模式视图相关性发现网络
Huiqing Wang1, Xiao Han1, Jianxue Ren1
1College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China.
Mathematical biosciences and engineering : MBE
|February 2, 2024
概括
我们开发了MDCADON,这是一个集成多omics数据的深度学习模型,用于改进卵巢癌预后预测. 这种方法增强了这种复杂疾病患者的生存分析和治疗计划.
科学领域:
- 在瘤学瘤学.
- 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:
- 卵巢癌呈现出各种临床病理学和分子特征,在诊断时通常具有先进的扩散.
- 早期诊断和预后预测对于理解病变发生和改善治疗结果至关重要.
- 卵巢癌中的多个OMIC数据呈现异质性,这对现有的整合方法构成了挑战.
研究的目的:
- 提出一种新的深度学习模型,MDCADON,用于整合多omics数据来预测卵巢癌的预后.
- 为了解决现有方法在处理多omics数据中的变化和相互关联方面的局限性.
- 增强生存分析和指导卵巢癌患者的治疗策略.
主要方法:
- 使用随机森林和LASSO回归对mRNA表达,DNA甲基化,miRNA表达和拷贝数变异 (CNV) 的特征选择.
- 一个用于学习的多模态深度神经网络,来自OMIC和临床数据的特征表示.
- 一个交叉模式的视图相关性发现网络来构建一个多omics发现张量来探索相互关系.
主要成果:
- 与现有的方法相比,MDCADON在预测卵巢癌预后方面表现优越.
- 该模型能够进行准确的生存分析,有助于确定患者的后续治疗计划.
- 基因本体学 (GO) 术语和途径分析确定了关键基因,并揭示了潜在的卵巢癌机制.
结论:
- 通过有效整合多omics数据,MDCADON提供了一种强大的新方法来预测卵巢癌的预后.
- 该模型探索OMIC间相关性的能力为疾病机制提供了洞察力.
- 这些发现支持改善卵巢癌的临床决策和治疗指导.
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