• Understand the genetic basis and molecular mechanisms of rare diseases.
• Explore public bioinformatics databases for disease gene identification and functional annotation.
• Apply network biology and pathway enrichment approaches to analyze disease-associated genes.
• Learn to construct and interpret protein–protein interaction networks using Cytoscape and STRING.
• Understand the role of Artificial Intelligence and machine learning in disease gene prioritization and biomarker discovery.
• Explore emerging computational approaches for precision genomics and rare disease research.

📅 Day 1: Foundations of Rare Disease Bioinformatics and Disease Gene Discovery

• Introduction to rare diseases and their genetic basis
• Understanding disease-associated genes and molecular mechanisms
• Role of bioinformatics in rare disease research
• Overview of public biological databases: OMIM, ClinVar, NCBI Gene, Ensembl, UniProt, GeneCards, and DisGeNET
• Gene annotation and functional information retrieval
• Introduction to sequence similarity search using BLAST
• Identifying candidate disease genes using bioinformatics databases

Exploring Rare Disease Genes Using Public Bioinformatics Databases

📅 Day 2: Network Biology and Functional Analysis of Rare Disease Genes

• Gene Ontology (GO) and functional enrichment analysis
• Protein–protein interaction (PPI) network analysis
• Network biology concepts for disease gene discovery
• Biological network visualization using Cytoscape
• Pathway databases: KEGG, Reactome, and WikiPathways
• Identification of hub genes and key biological pathways
• Functional interpretation of disease-associated molecular networks

Network Construction and Pathway Analysis Using Cytoscape

📅 Day 3: AI-Assisted Disease Gene Prioritization and Predictive Bioinformatics

• Artificial Intelligence in rare disease research
• AI-assisted disease gene prioritization and biomarker discovery
• Machine learning approaches for disease classification
• Integrating genomic, functional, and network biology data for disease prediction
• Explainable AI (XAI) for biological interpretation
• Use of Generative AI and Large Language Models (LLMs) for literature mining and biological knowledge discovery
• Future trends: network medicine, multi-omics integration, systems biology, precision medicine, and AI-assisted drug target identification

AI-Based Candidate Gene Prioritization and Disease Prediction

• Understand the role of bioinformatics in rare disease genomics and gene discovery.
• Retrieve and interpret disease-associated genomic information from public biological databases.
• Perform functional annotation, network biology analysis, and pathway enrichment of candidate genes.
• Construct and analyze protein–protein interaction networks using Cytoscape and related bioinformatics tools.
• Apply AI and machine learning techniques for disease gene prioritization and biomarker identification.
• Integrate genomic, functional, and network-based information to support rare disease research.
• Gain practical experience with widely used bioinformatics databases, network analysis platforms, and AI-enabled computational workflows for precision genomics.

• Certificate of Participation upon successful completion of the workshop.
• Hands-on Google Colab Notebooks covering disease gene discovery, network biology, and AI-based gene prioritization.
• Cytoscape Network Analysis Files for protein–protein interaction network construction and visualization.
• AI-Based Disease Gene Prioritization Workflow developed during the hands-on sessions using Google Colab.
• Workshop Presentation Slides and Learning Materials for future reference.
• Hands-on Datasets and Practice Exercises for independent learning after the workshop.