现象型驱动的数据增强方法用于转录数据
Nikita Janakarajan1,2, Mara Graziani1, María Rodríguez Martínez1
1AI for Scientific Discovery, IBM Research Europe, Rüschlikon 8803, Switzerland.
Bioinformatics advances
|June 9, 2025
概括
我们开发了新的基于表型的数据增强方法,用于高维的转录组数据,在癌症研究中提高了5-15%的患者分层,并提供了对最佳数据增强策略的见解.
科学领域:
- 生物医学数据科学是生物医学数据科学.
- 计算生物学是一种计算生物学.
- 在基因组学中的机器学习.
背景情况:
- 高维的转录组数据对监督学习提出了挑战,包括过度拟合和糟糕的泛化.
- 现有的转录学数据数据增强方法通常是计算密集型或产生有限的样本多样性.
- 类不平衡和较小的样本大小是转录组数据集中的常见问题,阻碍了模型性能.
研究的目的:
- 为转录基因数据引入新的表型驱动的数据增强方法.
- 解决监督学习任务中高维度,过度拟合和有限泛化的挑战.
- 在癌症转录组学研究中提高患者分层的准确性.
主要方法:
- 开发了两种类型的表型驱动数据增强:签名依赖和签名独立的方法.
- 签名依赖方法利用基因签名进行非参数数据增强.
- 独立于签名的方法适应已建立的Gamma-Poisson和Poisson采样技术来获取基因表达数据.
主要成果:
- 应用增强方法对结肠直肠癌和乳腺癌的转录数据.
- 与现有的增强方法相比,证明患者分层改善了5-15%.
- 通过有外部验证的歧视性和生成性实验,展示了增强的模型通用化和减少过拟合.
结论:
- 由表型驱动的数据增强有效地增强了对转录基因数据的监督学习.
- 提出的方法为数据增强提供了计算效率高且多样化的方法.
- 过度增加可能会产生有限的益处,这突显了战略应用的重要性.
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