识别特征的深度学习揭示了质母细胞瘤和结肠腺癌中独特的驱动基因程序和突变过程
Muhammad Zubair1, Jianqiang Li1, Jun Qian1
1College of Computer Science, Beijing University of Technology, Beijing 100124, China.
Computational biology and chemistry
|March 1, 2026
概括
ResMLP-GL通过整合突变特征和功能特征,准确地预测癌症驱动突变,优于现有的方法. 这种方法为精密瘤学提供了可解释的,特定于组织的见解.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 癌症研究 癌症研究
背景情况:
- 精确识别癌症驱动突变对于精确瘤学至关重要,但由于乘客突变和复杂的突变过程,具有挑战性.
- 现有的方法往往难以完全捕捉突变特征的细微差别及其对驱动基因识别的影响.
研究的目的:
- 开发和验证ResMLP-GL,一种新的签名感知剩余多层感知子,用于准确的变种级癌症驱动器预测.
- 整合COSMIC SBS (索马特单基替代) 语境概率向量,具有广泛的功能和序列特征,用于增强预测.
- 提供一种可解释的模型,阐明组织特异性突变过程及其与驱动突变的关系.
主要方法:
- 开发了ResMLP-GL,这是一个剩余的多层感知子,包含投射剩余块和功能智能门模块.
- 集成的COSMIC SBS上下文概率,拥有100多个功能和序列特征.
- 使用Optuna进行超参数优化和ADASYN解决类不平衡.
- 在TCGA GBM/COAD外体和独立的ICGC队列上训练并测试了模型.
主要成果:
- ResMLP-GL在持有数据上的AUC为0.949,在独立的ICGC队列上的AUC为0.921,在CHASMplus,OncodriveFML和MutSigCV的表现上表现出色.
- SHAP分析显示,功能分数 (REVEL,AlphaMissense,CADD) 和SBS概率是预测的关键驱动因素.
- 该模型确定了特定于组织的,与签名一致的驱动程序,并证明了驾驶员负担分层生存.
结论:
- ResMLP-GL通过利用剩余门的MLP和定量突变签名背景,提供最先进的,可解释的癌症驱动器预测.
- 明确纳入突变过程背景的方法补充和超越了仅依赖于复发或功能影响的方法.
- 该方法提供了一个强大的框架,用于解决驱动器突变预测中的组织特异性挑战,促进可重复的精确瘤学研究.
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