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

• URL: http://arxiv.org/abs/2501.01458v1
• Date: Tue, 31 Dec 2024 07:37:34 GMT
• Title: GAN-TAT: A Novel Framework Using Protein Interaction Networks in Druggable Gene Identification
• Authors: George Yuanji Wang, Srisharan Murugesan, Aditya Prince Rohatgi,
• Abstract summary: This study proposes GAN-TAT, a framework utilizing an advanced graph embedding technology, ImGNGA, 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.
• Score: 0.0
• License:
• 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).

Related papers

• Graph Diffusion Network for Drug-Gene Prediction [38.00034058447254]
  We introduce a graph diffusion network for drug-gene prediction (GDNDGP) It employs meta-path-based homogeneous graph learning to capture drug-drug and gene-gene relationships. Second, it incorporates a parallel diffusion network that generates hard negative samples during training, eliminating the need for exhaustive negative sample retrieval.
  arXiv  Detail & Related papers  (2025-02-13T13:54:58Z)
• scGSDR: Harnessing Gene Semantics for Single-Cell Pharmacological Profiling [5.831554646284266]
  scGSDR is a model that integrates two computational pipelines grounded in the knowledge of cellular states and gene signaling pathways. scGSDR enhances predictive performance by incorporating gene semantics and employs an interpretability module. The model's application has extended from single-drug predictions to scenarios involving drug combinations.
  arXiv  Detail & Related papers  (2025-02-02T15:43:20Z)
• GramSeq-DTA: A grammar-based drug-target affinity prediction approach fusing gene expression information [1.2289361708127877]
  We propose GramSeq-DTA, which integrates chemical perturbation information with the structural information of drugs and targets. Our approach outperforms the current state-of-the-art DTA prediction models when validated on widely used DTA datasets.
  arXiv  Detail & Related papers  (2024-11-03T03:17:09Z)
• A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction [85.2792480737546]
  Existing methods fail to utilize global protein information during DTI prediction. Cross-field information fusion strategy is employed to acquire local and global protein information. Siamese drug-target interaction SiamDTI prediction method achieves higher accuracy levels than other state-of-the-art (SOTA) methods on novel drugs and targets.
  arXiv  Detail & Related papers  (2024-05-23T13:25:20Z)
• Multiscale Topology in Interactomic Network: From Transcriptome to Antiaddiction Drug Repurposing [0.3683202928838613]
  The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment.
  arXiv  Detail & Related papers  (2023-12-03T04:01:38Z)
• Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network [69.16939798838159]
  We propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.
  arXiv  Detail & Related papers  (2023-11-15T06:34:00Z)
• PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps [4.590060921188914]
  Key aspect of drug discovery involves identifying novel drug-target (DT) interactions. Protein-ligand interactions exhibit a continuum of binding strengths, known as binding affinity. We propose novel enhancements to enhance their performance.
  arXiv  Detail & Related papers  (2023-10-06T05:00:25Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction [127.43571146741984]
  Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery. wet experiments remain the most reliable method, but they are time-consuming and resource-intensive. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
  arXiv  Detail & Related papers  (2022-06-20T14:53:25Z)
• DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations [90.27736364704108]
  We present DrugOOD, a systematic OOD dataset curator and benchmark for AI-aided drug discovery. DrugOOD comes with an open-source Python package that fully automates benchmarking processes. We focus on one of the most crucial problems in AIDD: drug target binding affinity prediction.
  arXiv  Detail & Related papers  (2022-01-24T12:32:48Z)
• Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer [98.8319016075089]
  We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture. Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively. IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
  arXiv  Detail & Related papers  (2021-10-14T13:28:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.