用图嵌入直接整合蛋白互作网络，精准识别可成药基因
GAN-TAT: A Novel Framework Using Protein Interaction Networks in Druggable Gene Identification
- 基于ImGAGN图嵌入技术，直接融合高维稀疏蛋白互作网络
- 在Tclin数据集上达0.951的AUC-ROC，优于已有方法
- 结果获临床证据支持，适合药物靶点发现研究者使用
识别可成药基因对新药研发至关重要。随着高质量数据的积累，计算方法已成为重要工具。蛋白互作网络（PIN）虽有价值，但因高维度和稀疏性难以应用，以往方法依赖间接整合导致信息损失。本研究提出GAN-TAT框架，采用先进图嵌入技术ImGAGN，直接整合PIN用于可成药基因推断。在三个Pharos数据集上测试，GAN-TAT在Tclin数据集上达到最高0.951的AUC-ROC。进一步评估显示其预测结果获得临床证据支持，凸显其在药物基因组学中的实际应用潜力。该研究为直接利用PIN提供了方法学尝试，拓展了新药靶点开发的解决方案。源代码已公开于https://github.com/george-yuanji-wang/GAN-TAT。
原文摘要 · Abstract (English)
Identifying druggable genes is essential for developing effective pharmaceuticals. With the availability of extensive, high-quality data, computational methods have become a significant asset. Protein Interaction Network (PIN) is valuable but challenging to implement due to its high dimensionality and sparsity. Previous methods relied on indirect integration, leading to resolution loss. This study proposes GAN-TAT, a framework utilizing an advanced graph embedding technology, ImGAGN, to directly integrate PIN for druggable gene inference work. Tested on three Pharos datasets, GAN-TAT achieved the highest AUC-ROC score of 0.951 on Tclin. Further evaluation shows that GAN-TAT's predictions are supported by clinical evidence, highlighting its potential practical applications in pharmacogenomics. This research represents a methodological attempt with the direct utilization of PIN, expanding potential new solutions for developing drug targets. The source code of GAN-TAT is available at (https://github.com/george-yuanji-wang/GAN-TAT).
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