About Course
“Bioinformatics for Cancer Biology & Network Pharmacology with Publication”
ক্যান্সার গবেষণায় হাজারো biological data থেকে গুরুত্বপূর্ণ gene, therapeutic target এবং potential drug candidate শনাক্ত করাই বড় চ্যালেঞ্জ।
Innovative Research Center (IRC)-এ শেখানো হবে একটি complete research workflow। Real patient-derived cancer data থেকে gene identification → target gene/protein selection → network analysis → CADD-based drug discovery পর্যন্ত।
অর্থাৎ, শুধু software নয়; data থেকে meaningful target identify করে publication-oriented drug discovery research কীভাবে করা যায়, সেটাই হবে মূল focus।
Course Curriculum – 6 Classes
Class 01 | Introduction to Transcriptomics & GEO2R Analysis
Class 02 | Differentially Expressed Genes (DEGs) Analysis Using R
Class 03 | Protein-Protein Interaction (PPI) & Cytoscape Analysis
Class 04 | Enrichment Analysis (GO & KEGG Pathway Analysis)
Class 05 | Identification & Visualization of Transcriptional & Regulatory Networks
Class 06 | PAN Cancer Analysis
Tools & Databases Covered
R • RStudio • GEO • ArrayExpress • LIMMA • DESeq2 • STRING • IMEx • Cytoscape • CytoHubba • DAVID • Enrichr • GeneCodis • JASPAR • TarBase • NetworkAnalyst • DrugBank • PubChem • AutoDock Vina • SwissADME • pkCSM
কারা এই Course করতে পারবেন?
• Undergraduate & Postgraduate Students
• Early-Career Researchers
• Pharmacy, Biotechnology & Life Science Researchers
• Cancer Biology & Drug Discovery Enthusiasts
• যারা Bioinformatics ও Computational Research শিখতে চান
Advanced programming জানা বাধ্যতামূলক নয়।
Course Benefits
• Recorded Class Access
• Certificate of Completion
• Hands-on Project Experience
• Software & Tools Will Be Provided
• Publication-Oriented Learning
Dedicated students-দের Scopus-indexed Q1 journal-এ publication-এর লক্ষ্যে publication এ কাজ করার সুযোগ থাকবে।
Class হবে প্রতি Friday & Saturday
8:00 PM – 11:00 PM
Registration Deadline: 07 October, 2026
Class Start: 09 October, 2026
Registration Link: https://forms.gle/Z752zQAR8hic73q8A
Course Content
Day 1: Advanced Python Programming
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Recap of Python basics
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Functional programming: lambda, map(), filter(), reduce()
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Comprehensions: list, dict, set
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Modules, packages, and virtual environments
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Error handling & decorators
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Hands-on: Build a custom Python utility function
Day 2: Data Handling with Pandas & NumPy
Day 3: Introduction to Machine Learning
Day 4: Classification & Clustering Algorithms
Day 5: Real-world Project & Deployment Intro
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