👥 Authors: Milad Torabi, Possenti Francesca
🔹 Sapienza University, Bioinformatics and Network Medicine
📅 January 16, 2025
📝 Overview:
Non-alcoholic steatohepatitis (NASH) is a complex liver disorder characterized by fat accumulation, inflammation, and hepatocellular injury. As the prevalence of NASH rises globally, there is an urgent need to identify putative disease genes and explore potential drug repurposing strategies. This project utilizes network science methods and bioinformatics algorithms to analyze NASH-related genes and repurpose existing drugs as potential therapeutic candidates.
🚀 Our goal: Identify putative disease genes & explore drug repurposing using network-based algorithms.
🎯 Project Objectives
🔬 Disease Gene Identification:
- Identify putative disease-associated genes using PPI network analysis.
- Utilize network-based algorithms (DIAMOnD, DIAble, Diffusion) to identify key genes related to NASH.
- Integrate multi-omics data (proteomics and genomics) to explore disease mechanisms.
- Implement drug repurposing strategies by screening approved drugs against the identified genes.
- Provide insights into novel therapeutic avenues for NASH treatment.
💊 Drug Repurposing Approach:
- Screen FDA-approved drugs
- Identify potential therapeutic candidates
- Accelerate the drug discovery process
📂 Project Root
├── 📘 Report.pdf
├── 📄 README.md (This File)
├── 📒 Results Folder
└── 📒 Main - Project.ipynb (Jupyter Notebook)
🛠 Methodology & Workflow:
📥 Step 1: Data Collection
- Protein-Protein Interaction (PPI) Network: Retrieved from BioGRID database.
- Gene-Disease Associations (GDAs): Extracted from DisGeNET (Disease Code: C2711227).
🕸 Step 2: Network Construction & Analysis
- Built PPI network from BioGRID interactome.
- Filtered data to include only human physical interactions.
- Identified the largest connected component (LCC) within the network.
🧪 Step 3: Disease Gene Identification
- Applied DIAMOnD, DIAble, and Diffusion algorithms to identify putative disease genes.
- Prioritized genes based on their connectivity and biological relevance.
💊 Step 4: Drug Repurposing
- Mapped identified genes to known drug-target interactions.
- Screened FDA-approved drugs for potential efficacy against NASH-related pathways.
🏗 Tech Stack & Tools
🖥 Programming Language: Python
📚 Libraries: pandas, numpy, networkx, matplotlib, seaborn
🗄 Databases: BioGRID and DisGeNET databases for gene and interaction data
📊 Algorithms: DIAMOnD, DIAble, Diffusion(Graph-based algorithms for network analysis)
📊 Results & Insights
✔ Identified key genes potentially involved in NASH pathogenesis.
✔ Mapped existing drugs for repurposing
✔ Contributed to advancing bioinformatics-driven approaches for liver disease research
🔍 Potential Impact:
🚀 Accelerates drug discovery
🩺 Provides therapeutic targets
🔬 Advances precision medicine for NASH
🔮 Future Work
- Expand datasets with multi-omics data
- Validate findings via experimental studies
- Incorporate deep learning for better gene-disease association predictions