具有有限数据的深度学习:用于转录基因生存预测的转移学习方法.
G Sabbatini1, A M Lombardi2, S Bianco1
1aizoOn Technology & Consulting, Strada del Lionetto, 6, 10146, Torino, TO, Italy.
Computers in biology and medicine
|March 11, 2026
概括
转移学习显著提高了使用转录组数据的癌症生存预测,特别是在小患者队列中. 这种方法利用癌症中共享的分子模式,以获得更强大的无疾病生存模型.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 在分析复杂的癌症转录基因数据方面表现有前途.
- 数据稀缺性和异质性限制了癌症研究中的DNN.
- 转移学习 (TL) 在癌症生存预测中未得到充分利用,尽管它在其他领域取得了成功.
研究的目的:
- 用癌症基因组图谱 (TCGA) RNA-seq数据评估转移学习以预测无疾病生存率.
- 将TL模型与瘤特定基线模型进行比较.
- 评估TL模型在癌症研究中的生物相关性.
主要方法:
- 利用了来自27种瘤类型的7509名患者的RNA-seq数据.
- 训练了一种泛癌DNN,并将TL模型 (预训练和微调) 与独立的瘤特异模型进行比较.
- 通过LIME-like可解释性和基因组丰富分析评估使用一致性指数和生物相关性的性能.
主要成果:
- 在27种瘤类型中,转移学习在24种瘤中表现优于瘤特异模型.
- 在较小的患者队伍中,表现的增长最为显著.
- 微调调整将一般的癌症模式与特定的瘤数据相适应,通过丰富分析证实了这一点.
结论:
- 转移学习是一种强大的策略,可以从转录组数据中改善癌症生存预测,特别是在数据有限的场景中.
- TL有效地利用了跨不同癌症类型的共同分子模式.
- 这项工作支持TL作为生存建模的强大方法,并推动分子瘤学的基础模型的开发.
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