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

TL;DR

GAN-TAT introduces a novel graph embedding framework that directly utilizes Protein Interaction Networks to improve druggable gene prediction, achieving high accuracy and clinical relevance.

Contribution

This work presents GAN-TAT, a new framework that directly integrates PIN data using ImGAGN for druggable gene inference, overcoming previous indirect integration limitations.

Findings

Achieved an AUC-ROC score of 0.951 on Tclin dataset.

Predictions supported by clinical evidence.

Demonstrated potential for practical pharmacogenomics applications.

Abstract

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).