MLGCN-Driver:一种基于多层图形卷积神经网络的癌症驱动基因识别方法
Pi-Jing Wei1, Jingxin Zhou1, Rui-Fen Cao2
1Key Laboratory of Intelligent Computing Signal Processing of Ministry of Education, Institute of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China.
BMC bioinformatics
|October 1, 2025
概括
识别癌症驱动基因对于了解癌症进展至关重要. 本研究介绍了MLGCN-Driver,这是一种使用多层图形卷积网络的新方法,通过分析高阶网络特征来有效识别驱动基因.
科学领域:
- 计算生物学 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:
- 癌症的进展是由驱动基因的突变驱动的.
- 鉴定癌症驱动基因是一个关键的研究领域.
- 现有的方法往往忽略了高阶网络特征.
研究的目的:
- 提出一种新的方法,MLGCN-Driver,用于增强癌症驱动基因识别.
- 将高阶网络特征纳入驱动基因预测中.
- 为了利用多主题和拓网络数据.
主要方法:
- 开发了MLGCN-Driver,这是一个多层图形卷积神经网络 (GCN) 模型.
- 利用初始剩余连接和身份映射来学习多omics特性.
- 使用 node2vec 算法提取拓结构特征.
- 综合生物和拓特征用于驱动基因概率计算.
主要成果:
- MLGCN-Driver有效地从生物多态和网络拓特征中学习.
- 剩余连接和身份映射可以防止特征过度平滑.
- 该方法根据综合特征计算驱动基因概率.
结论:
- MLGCN-Driver在泛癌和特定癌症数据集上的驱动基因识别方面表现出卓越的表现.
- 该方法在ROC曲线下的面积 (AUC) 和精度召回曲线下的面积 (AUPRC) 中取得了出色的结果.
- MLGCN-Driver在驱动基因识别方面表现优于最先进的方法.
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