通过转换器驱动的图形表示学习在生物网络中对癌症基因的可解释识别
Xiaorui Su1,2,3, Pengwei Hu1,2, Dongxu Li1,2
1Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China.
Nature biomedical engineering
|January 9, 2025
概括
一个新的基于变压器的模型使用图形表示学习和多omics数据准确地预测癌症基因. 这种可解释的方法增强了对基因调节机制的理解,并有助于发现新的癌症基因.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 图形表示学习用于从生物网络中识别癌症基因.
- 现有的方法在整合性网络分析中缺乏可解释性和通用性.
研究的目的:
- 为准确的癌症基因预测开发一种可解释和可概括的基于变压器的模型.
- 将多学科数据与生物网络拓集成,以进行增强的分析.
主要方法:
- 杆图表表示学习和变压器架构.
- 集成的多学科数据与同质和异质的生物相互作用网络.
- 能够解释多原子和结构特征的重要性.
主要成果:
- 在跨越各种生物网络 (miRNA-蛋白,TF-蛋白,TF-miRNA) 的癌症基因预测方面取得了最先进的表现.
- 在泛癌和癌症特异性分析中表现出高准确性.
- 预测了57个新的癌症基因候选者,包括3个以前未识别的基因,来自8个泛癌数据集中的4729个未标记的基因.
结论:
- 开发的模型为癌症基因发现提供了更好的解释性和概括性.
- 促进对基因调节机制的理解.
- 帮助识别新的与癌症相关的基因.
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