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

Autor:	Wang, George Yuanji, Murugesan, Srisharan, Rohatgi, Aditya Prince
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Quantitative Biology - Quantitative Methods
Druh dokumentu:	Working Paper
Popis:	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). Comment: 4 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2501.01458 Zobrazit plný text záznamu View this record from Arxiv