疾病网:一种转移学习方法来对非传染性疾病进行分类
Steven Gore1, Bailey Meche2, Danyang Shao1
1Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, USA.
BMC bioinformatics
|March 12, 2024
概括
通过神经网络进行转移学习,有效地预测使用表观遗传标记的关节炎,喘和精神分裂症等非传染性疾病 (NCD). 这种方法可以提高复杂疾病的诊断准确性,即使数据有限.
科学领域:
- 生物医学信息学是生物医学信息学.
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
背景情况:
- 非传染性疾病 (NCD) 是全球卫生面临的一个重大挑战.
- 包括DNA甲基化在内的表观遗传修饰是NCD的有希望的生物标志物.
- 神经网络可以模拟复杂的生物数据,但在有限的NCD数据集中面临挑战.
研究的目的:
- 为非癌症非传染性疾病开发准确的诊断和预测模型.
- 探索转移学习对NCD建模的实用性.
- 通过基于概念的解释来研究特征的重要性.
主要方法:
- 利用在癌症数据上预先训练的神经网络进行转移学习方法.
- 应用该模型从血液样本预测关节炎,喘和精神分裂症.
- 用概念激活矢量 (TCAV) 进行实用测试,以提高模型的可解释性.
主要成果:
- 在预测所选的非传染性疾病方面获得了94.5%的高整体准确性 (f-测量).
- 证明了转移学习在NCD预测中的有效性.
- 通过TCAV识别了通过TCAV影响模型性能的主要样本来源.
结论:
- 转移学习是建立强大的NCD预测模型的可行和有效策略.
- 表观遗传标记物与神经网络相结合,显示出NCD诊断的巨大潜力.
- 像TCAV这样的可解释性方法可以指导NCD模型培训数据集的改进.
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