About Workshop
Rare diseases affect millions of people worldwide, yet identifying their underlying genetic causes remains a significant scientific challenge. Advances in bioinformatics, network biology, and Artificial Intelligence (AI) have transformed rare disease research by enabling efficient identification of disease-associated genes, functional analysis of biological pathways, and AI-assisted prioritization of candidate biomarkers.
This 3-day international workshop introduces participants to modern computational approaches for rare disease genomics using publicly available biological databases, network biology tools, pathway enrichment analysis, and machine learning techniques. Through practical hands-on sessions using Google Colab, Cytoscape, STRING, and public genomic resources, participants will learn how to identify candidate disease genes, analyze molecular interaction networks, perform functional interpretation, and apply AI-driven approaches for disease gene prioritization and biomarker discovery.
Aim
To provide participants with a practical understanding of how bioinformatics, network biology, and Artificial Intelligence can be integrated to identify, analyze, and prioritize disease-associated genes for rare disease research and precision genomics.
What Participants Will Learn
- 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.
Structure
📅 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
Participants will use Google Colab along with public bioinformatics resources such as NCBI Gene, OMIM, GeneCards, UniProt, Ensembl, and BLAST to identify genes associated with a selected rare disease. They will retrieve gene and protein information, perform functional annotation, explore disease associations, and compile a list of candidate genes for downstream bioinformatics analysis.📅 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
Participants will use STRING and Cytoscape to construct a protein–protein interaction network from candidate disease genes. They will identify hub genes, visualize interaction networks, and perform Gene Ontology and KEGG/Reactome pathway enrichment analysis using tools such as DAVID, Enrichr, or g:Profiler to discover biological processes and pathways associated with the selected rare disease.📅 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
Participants will use Google Colab to integrate functional annotations, pathway enrichment results, and network-derived features from rare disease-associated genes. They will build a simple machine learning model to prioritize candidate disease genes, identify potential biomarkers, evaluate prediction performance, and generate a ranked list of genes for further biological investigation and therapeutic target discovery.Important Dates
Registration Ends
4:00 PM
Workshop Dates
2026-09-28
4:30 PM
4:30 PM
What You Will Gain
- 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.
Outcomes
- 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.
Who Should Attend
This workshop is designed for undergraduate and postgraduate students, Ph.D. scholars and research fellows, faculty members and academicians, bioinformatics and computational biology researchers, genomics and molecular biology scientists, biotechnology and pharmaceutical professionals, data scientists working in life sciences, healthcare researchers, AI and machine learning enthusiasts, and industry professionals interested in genomics, precision medicine, and computational biology.
Deliverables
- 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.