AHDSN:一个能够引起注意力的混合深度序列网络,用于从多omics数据中预测癌症存活率
Ambika Hazarika1, Ansuman Kumar1, Anindya Halder2
1Department of Computer Application, North-Eastern Hill University, Tura Campus, Tura, Meghalaya, 794002, India.
概括
这项研究引入了一种新的注意力启用混合深度序列网络 (AHDSN),用于使用多omics数据预测癌症患者的生存率. 该AHDSN方法准确预测整体存活率,在五种癌症类型中表现优于现有的方法.
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
- 机器学习在瘤学中
背景情况:
- 癌症生存率预测对于患者管理和治疗策略至关重要.
- 多omics数据提供了全面的分子概况,以提高预测能力.
- 现有的生存预测模型通常集中在固定的时间点上,限制了它们的范围.
研究的目的:
- 引入一种新的深度学习模型,即注意力启用混合深度序列网络 (AHDSN),用于准确预测癌症存活率.
- 通过使用全面的多omics数据,预测整个随访期的整体生存率.
- 根据最先进的方法评估AHDSN的性能.
主要方法:
- 利用长期短期内存,双向门式反复单元和注意力机制,从多omics数据中提取特征.
- 使用密集层与软max激活用于分类.
- 应用随机过量抽样 (ROS) 和合成少数人过量抽样技术 (SMOTE) 解决阶级不平衡问题.
- 评估了质母细胞瘤,结肠,乳腺,脏和肺癌多omics数据集的性能.
主要成果:
- 与现有方法相比,AHDSN方法实现了更高的准确性,精度,回忆和F1得分.
- 准确度在80.00%至98.33%之间,取决于所使用的数据集和过量采样技术.
- 信任区间测试证实AHDSN的较低的错误率和较小的错误边界.
- 沙普利添加剂的解释和热图为特征的重要性提供了洞察力.
结论:
- 拟议的AHDSN方法在使用多omics数据预测癌症存活率方面取得了重大进展.
- 混合深度序列架构有效地提取潜在特征,以准确地预测整体生存率.
- 该模型的可解释性特征提供了有价值的洞察力,可以了解个别omics特征的贡献.
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