标题： GAN-TAT：一种在可药物基因识别中使用蛋白质相互作用网络的新框架 显示英文标题

标题： GAN-TAT: A Novel Framework Using Protein Interaction Networks in Druggable Gene Identification

摘要： 识别可药物基因对于开发有效的药物至关重要。 随着大量高质量数据的可用性，计算方法已成为重要的工具。 蛋白质相互作用网络（PIN）很有价值，但由于其高维性和稀疏性，实现起来具有挑战性。 以前的方法依赖于间接整合，导致分辨率损失。 本研究提出了GAN-TAT，一种利用先进图嵌入技术ImGAGN的框架，直接整合PIN用于可药物基因推断工作。 在三个Pharos数据集上测试，GAN-TAT在Tclin上达到了0.951的最高AUC-ROC分数。 进一步评估表明，GAN-TAT的预测得到了临床证据的支持，突显了其在药基因组学中的潜在实际应用。 这项研究代表了一种方法论上的尝试，直接利用PIN，拓展了开发药物靶点的潜在新解决方案。 GAN-TAT的源代码可在（https://github.com/george-yuanji-wang/GAN-TAT）获取。 显示英文摘要

摘要： 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).