ECD-CDGI：癌症驱动器基因鉴定的有效能量约束的扩散模型
ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification
影响因子:3.60000
分区:生物学2区 / 生化研究方法2区 数学与计算生物学2区
发表日期:2024 Aug
作者:
Tao Wang, Linlin Zhuo, Yifan Chen, Xiangzheng Fu, Xiangxiang Zeng, Quan Zou
摘要
癌症驱动基因（CDG）的鉴定引起的挑战是由于基因之间的复杂相互依赖性以及测量误差和噪声的影响。我们提出了一种新型的能量约束扩散（ECD）的模型，用于识别CDG，称为ECD-CDGI。该模型是第一个通过将ECD技术与注意机制相结合的设计ECD注意编码器的模型。 ECD注意的编码器擅长生成鲁棒基因表示，这些基因表示，这些基因表示基因之间的复杂相互依赖性，同时减少了数据噪声的影响。我们通过图形变压器从基因基因网络提取到这些基因表示，将拓扑嵌入从基因基因网络中提取。我们在三种测试场景中进行了广泛的实验。广泛的实验表明，ECD-CDGI模型不仅具有熟练识别已知CDG的能力，而且还具有有效地发现未知的潜在CDG。此外，与基于GNN的方法相比，ECD-CDGI模型的现有基因基因网络的限制更少，从而增强了其鉴定CDG的能力。此外，ECD-CDGI是开源的，可以自由使用。我们还启动了该模型作为一种免费的在线工具，专门为加快针对CDG识别的研究工作而制定。
Abstract
The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.