Systems Biology of Disease: Multi-Omics Integration and Network Analysis
Systems Biology of Disease is an intermediate-level, 6-week online internship by NSTC.
- ModeOnline, Project-Based Internship
- LevelBeginner
- Fee₹5,499
| Attribute | Detail |
|---|---|
| Format | Online, Project-Based Internship |
| Level | Beginner |
| Certification | Internship e-Certificate upon Successful Completion |
| Fee | ₹5499 / $99 |
| Tools | Cancer and Drug Resistance Neurodegenerative Disorders Cardiovascular and Metabolic Diseases Infectious Diseases and Host–Pathogen Interactions Autoimmune and Inflammatory Disorders Oxidative Stress and Cellular Ageing Select a disease and retrieve suitable public transcriptomic datasets. Clean, normalise, and perform quality control of gene-expression data. Identify differentially expressed genes and generate visualisations. Perform Gene Ontology, KEGG, and Reactome enrichment analysis. Construct and analyse protein-interaction networks using STRING and Cytoscape. Integrate genes, pathways, modules, and regulators into a disease model. Google Colab, Python and R GEO and TCGA-Compatible Data DESeq2, edgeR and limma STRING and Cytoscape Enrichr, g:Profiler and clusterProfiler KEGG, Reactome and GitHub Differentially expressed gene list and visualisations Functional enrichment and pathway-analysis report STRING and Cytoscape protein-interaction network Hub-gene, functional-module, and regulator analysis Integrated systems-level disease model Analysis notebook, GitHub repository, report, and presentation Systems Biology Analysis of Breast Cancer Progression Multi-Omics Investigation of Alzheimer’s Disease Pathways Systems-Level Analysis of Drug Resistance in Lung Cancer Host–Pathogen Systems Biology of Viral Infection Systems Biology of Oxidative Stress and Cellular Ageing Integrated Gene and Pathway Analysis of Type 2 Diabetes |
About the Systems Biology of Disease: Multi-Omics Integration and Network Analysis Course
The Systems Biology of Disease internship is a six-week, project-based programme designed to help participants investigate diseases through the integrated analysis of genes, proteins, biological pathways, regulatory mechanisms, and multi-omics data.
Participants will work with publicly available transcriptomic datasets to identify differentially expressed genes, enriched biological processes, disease-associated pathways, protein-interaction networks, hub genes, and transcriptional regulators.
By combining gene-expression results with network and pathway information, participants will develop an integrated systems-level model explaining the molecular mechanisms involved in disease development, progression, treatment response, or drug resistance.
