由于基因之间错综复杂的相互依赖性以及测量误差和噪声的影响，癌症驱动基因（CDG）的识别提出了挑战。我们提出了一种基于能量约束扩散（ECD）的新型模型来识别 CDG，称为 ECD-CDGI。该模型是第一个将 ECD 技术与注意力机制相结合来设计 ECD-Attention 编码器的模型。 ECD-Attention 编码器擅长生成强大的基因表示，揭示基因之间复杂的相互依赖性，同时减少数据噪声的影响。我们通过图转换器将从基因-基因网络中提取的拓扑嵌入连接到这些基因表示。我们在三个测试场景中进行了广泛的实验。大量实验表明，ECD-CDGI模型不仅能够熟练识别已知的CDG，而且能够有效地发现未知的潜在CDG。此外，与基于 GNN 的方法相比，ECD-CDGI 模型受现有基因-基因网络的约束较少，从而增强了其识别 CDG 的能力。此外，ECD-CDGI 是开源的并且免费提供。我们还推出了该模型作为免费在线工具，专门用于加快 CDG 识别的研究工作。版权所有：© 2024 Wang 等人。这是一篇根据知识共享署名许可条款分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是注明原始作者和来源。

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.Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.