蛋白质化剂:利用转录组-蛋白质组不匹配来推断新的基因调控关系
Giulio Deangeli1, Maria Grazia Spillantini1, Pietro Liò2
1University of Cambridge, Department of Clinical Neurosciences, Clifford Allbutt Building, Hills Road, CB2 0HA Cambridge, UK.
bioRxiv : the preprint server for biology
|July 16, 2025
概括
一个深度学习平台的Proteomizer增强了从转录和miRNomic数据的蛋白质丰度预测,改进了差异基因表达分析. 它提供了对转录蛋白差异的见解,但需要在各种数据集中进行验证.
科学领域:
- 多领域的整合.
- 计算生物学是一种计算生物学.
- 基因组学中的深度学习
背景情况:
- 转录基因 (Tx) 和蛋白质基因 (Px) 档案显示了适度的相关性,限制了Tx数据对蛋白质丰度的有用性.
- 现有的方法很难从Tx和miRNomic (Mx) 数据中准确推断Px.
研究的目的:
- 介绍 Proteomizer,这是一个深度学习平台,可以从Tx和Mx配置文件中推断Px.
- 评估Proteomizer在改善差异基因表达分析方面的表现.
- 使用可解释AI (XAI) 调查Tx-Px差异.
主要方法:
- 在TCGA和CPTAC的8613个匹配的Tx-Mx-Px样本上训练了蛋白质化剂.
- 利用蒙特卡洛模拟来评估蛋白质化对差异表达的影响.
- 应用了XAI技术,并将预测与生物知识图进行比较.
主要成果:
- 实现了创纪录的Tx-Px相关性r=0.68.8.
- 显著提高差异性基因表达精度 (p值精度增加高达62倍).
- 对于miRNA基因下调调节预测,XAI确定了与ROC-AUC为0.74的调节关系.
结论:
- 蛋白质化剂代表了用于多原子集成的最先进的工具.
- 性能依赖于数据集,强调需要仔细验证.
- XAI提供了对转录蛋白水平差异的可解释的见解.
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