GAN-TAT: A Novel Framework Using Protein Interaction Networks in Druggable Gene Identification
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916549611225088 |
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| author | Wang, George Yuanji Murugesan, Srisharan Rohatgi, Aditya Prince |
| author_facet | Wang, George Yuanji Murugesan, Srisharan Rohatgi, Aditya Prince |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01458 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GAN-TAT: A Novel Framework Using Protein Interaction Networks in Druggable Gene Identification Wang, George Yuanji Murugesan, Srisharan Rohatgi, Aditya Prince Machine Learning Artificial Intelligence Quantitative Methods 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). |
| title | GAN-TAT: A Novel Framework Using Protein Interaction Networks in Druggable Gene Identification |
| topic | Machine Learning Artificial Intelligence Quantitative Methods |
| url | https://arxiv.org/abs/2501.01458 |