ECD-CDGI：一种高效的能量限制扩散模型，用于癌症驱动基因的识别

ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification

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PLoS Computational Biology

影响因子:3.6

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

DOI: 10.1371/journal.pcbi.1012400

摘要

癌症驱动基因（CDGs）的鉴定面临复杂的基因间相互依赖关系以及测量误差和噪声的影响。我们提出了一种基于能量限制扩散（ECD）的新型模型，用于识别CDGs，称为ECD-CDGI。该模型首次设计了结合ECD技术与注意力机制的ECD-注意力编码器。ECD-注意力编码器擅长生成鲁棒的基因表示，揭示基因间复杂的相互依赖关系，同时降低数据噪声的影响。我们将从基因-基因网络中通过图变换器提取的拓扑嵌入与这些基因表示连接起来。在三个测试场景中进行了大量实验。实验结果表明，ECD-CDGI模型不仅能有效识别已知的CDGs，还能高效发现未知潜在的CDGs。与基于GNN的方法相比，ECD-CDGI模型受现有基因-基因网络的限制更少，从而增强了其识别能力。此外，ECD-CDGI是开源的，并可免费使用。我们还将该模型作为一个免费在线工具，旨在加快针对CDGs鉴定的研究工作。

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.