Graph Attention Network-Based Prediction of Drug-Gene Interactions of Signal Transducer and Activator of
Shubhangini Chatterjee1, Pradeep Kumar Yadalam1
1Department of Periodontics, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND.
Abstract:
Introduction The signal transducer and activator of transcription-1 (STAT-1) are tightly controlled signaling pathways, with induced genes acting as positive and negative regulators. Persistent activation of the signal transducer and activator of transcription (STATs), particularly signal transducer and activator of transcription-3 (STAT-3) and signal transducer and activator of transcription-5 (STAT-5), is common in human tumors and cell lines. STAT molecules act as transcription factors, regulated by ligands like interferon-α (IFN-α), interferon-γ (IFN-γ), epidermal growth factor (EGF), platelet-derived growth factor (PDGF), interleukin-6 (IL-6) and interleukin-27 (IL-27). STAT-1 mutations can cause infections like periodontitis, a chronic inflammatory disease affecting gum tissue and bone. STAT-1 drug-gene interactions are being studied for therapeutic applications. Our study aims to predict drug-gene interactions of STAT-1 receptors in periodontal inflammation using graph attention networks (GATs). Methodology The study used a dataset of 215 drug-gene interactions to train and test a GAT model. The data was cleaned and normalized before being subjected to GATs using the Python library. Cytoscape and cytoHubba were used to visualize and analyze biological networks, including drug-gene interactome networks. The GAT model consisted of two graph attention layers, with the first layer producing eight features and the second layer aggregating outputs for binary classification. The model was trained using the Adam optimizer and CrossEntropyLoss function. Results The drug-gene interactome network, analyzed using Cytoscape, had 657 nodes, 1591 edges, and 4.755 neighbors. The predictive GAT model had low accuracy due to data availability and complexity. Conclusion The GAT model for drug-gene interactions in periodontal inflammation had low accuracy due to data limitations, complexity, and inability to capture all relevant features.
Insights
This study explored predicting drug-gene interactions for STAT-1 in periodontal inflammation using graph attention networks (GATs). The model showed low accuracy due to data limitations and complexity.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Genomics
Background:
- Signal transducer and activator of transcription-1 (STAT-1) pathways are crucial in cellular signaling and gene regulation.
- Aberrant STAT activation, particularly STAT-3 and STAT-5, is implicated in various human tumors.
- STAT-1 dysfunction is linked to conditions like periodontitis, a chronic inflammatory gum disease.
Purpose of the Study:
- To predict drug-gene interactions involving STAT-1 receptors in the context of periodontal inflammation.
- To apply graph attention networks (GATs) for modeling these complex biological interactions.
- To investigate the potential of computational approaches for understanding STAT-1 related diseases.
Main Methods:
- A dataset of 215 drug-gene interactions was utilized for training and testing a GAT model.
- Data preprocessing involved cleaning and normalization before GAT application.
- Network visualization and analysis were performed using Cytoscape and cytoHubba.
Main Results:
- The drug-gene interactome network comprised 657 nodes and 1591 edges.
- The predictive GAT model demonstrated low accuracy in predicting drug-gene interactions.
- Challenges included data availability, model complexity, and feature representation.
Conclusions:
- The GAT model's low accuracy highlights limitations in current data and methodologies for predicting STAT-1 drug-gene interactions in periodontal inflammation.
- Further research with more comprehensive datasets and advanced modeling techniques is warranted.
- Understanding these interactions is critical for developing targeted therapies for inflammatory diseases.
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