Completed

BIOINFORMATICS BOOTCAMP:
5-Day Intensive Program: Biopython & Machine Learning for Biological Data Science

📅 Dates

March 26 – 30, 2026

⏰ Time

7:30 PM – 9:00 PM (IST)

💻 Format

Online Hands-On

📚 Sessions

5 Intensive Days

Completed View Code on GitHub Contact Us

About the Bootcamp

This 5-Day Bioinformatics Bootcamp delivers an intensive, hands-on journey into biological data science using Python — covering the full spectrum from sequence analysis with Biopython through classical machine learning to state-of-the-art deep learning and pretrained protein/genomic language models. Participants work directly with real biological datasets at every stage, building reproducible pipelines that mirror workflows used in active research.

Designed for students, researchers, and life science professionals who want to confidently apply computational methods in genomics, proteomics, and biomedical research — no prior machine learning experience is required. All code is shared openly on GitHub so participants can revisit, remix, and build on workshop materials long after the bootcamp ends.

👥 Who Should Attend?

Perfect for undergraduate and postgraduate students, PhD scholars, and faculty in bioinformatics, biotechnology, life sciences, or computational biology who want to build practical ML skills. Industry professionals transitioning into computational biology will also find this a fast, structured way to get up to speed with modern AI-driven research methods.

📅 Workshop Schedule at a Glance

Day Date Topic
Day 1 Wednesday, March 26 Python Foundations for Biological Data
Day 2 Thursday, March 27 Biopython for Sequence Analysis
Day 3 Friday, March 28 Classical Machine Learning in Bioinformatics
Day 4 Saturday, March 29 Deep Learning in Bioinformatics
Day 5 Sunday, March 30 Pretrained Models, Advanced Applications & Ethics

Bootcamp Registration Fee

UG/PG Student

₹999 INR

PhD Scholar/Researcher

₹1499 INR

Others

₹1999 INR

International Participant

$30 USD

✓ Includes: Recordings • Study Materials • Certificate • GitHub Code Access

📚 Detailed Daily Curriculum

Day 1 — Python Foundations for Biological Data  |  Wednesday, March 26
Concepts Covered
  • Python fundamentals focused for biology: variables, data types, loops, conditionals
  • Functions and modular coding practices
  • File handling — reading and parsing FASTA format
  • String manipulation for biological sequence analysis
  • Writing reusable functions and error handling basics
Hands-On Practice
  • Read a FASTA file using pure Python
  • Extract sequence IDs and lengths
  • Count number of sequences in a dataset
  • Generate summary statistics from biological files
🎯 Mini Task
  • Read a FASTA file, calculate average sequence length, and output a summary report
Day 2 — Biopython for Sequence Analysis  |  Thursday, March 27
Concepts Covered
  • Biopython architecture and core design
  • Working with the Seq object and SeqIO module
  • Parsing FASTA files using SeqIO
  • Reverse complement and GC content calculation
  • DNA to protein translation and writing filtered FASTA files
Hands-On Practice
  • Compute reverse complements of DNA sequences
  • Calculate GC% across a dataset
  • Translate DNA sequences to protein
  • Save sequences greater than 500 bp into a new FASTA file
🎯 Mini Project
  • Build a mini sequence-processing pipeline: input FASTA → filter by length → compute GC% → export processed file
Day 3 — Classical Machine Learning in Bioinformatics  |  Friday, March 28
Core Models & Concepts
  • Logistic Regression, Random Forest, and Support Vector Machine (SVM)
  • Feature engineering: k-mer frequencies and amino acid composition
  • Data preprocessing and train-test split strategies
  • Cross-validation and model evaluation: Accuracy, Precision, Recall, ROC-AUC
Practical Workflow
  • Convert biological sequences into numerical features
  • Train multiple ML models on the same biological dataset
  • Compare model performance across metrics
  • Analyze feature importance using Random Forest
Day 4 — Deep Learning in Bioinformatics  |  Saturday, March 29
Neural Network Foundations
  • Neural network intuition and loss functions for biological tasks
  • Backpropagation — conceptual understanding without heavy math
  • Overfitting, regularization, and dropout strategies
Architectures Used in Bioinformatics
  • CNNs — Motif discovery and regulatory genomics
  • RNNs / LSTMs — Sequential biological data modeling
  • Transformers — Protein and DNA/RNA language models
Hands-On Practical
  • Build a simple neural network using PyTorch or Keras
  • Train a small biological classification model end-to-end
  • Compare deep learning vs classical ML performance on the same task
Day 5 — Pretrained Models, Advanced Applications & Ethics  |  Sunday, March 30
Pretrained Models & Transfer Learning
  • ESM — Protein embeddings from Meta AI
  • ProtBERT — BERT-style model for protein sequences
  • DNABERT — Genomic language modeling
  • AlphaFold — Conceptual overview and applications
🔬 Practical
  • Generate embeddings from a pretrained protein model
  • Use embeddings for clustering and downstream classification
Real-World AI Applications
  • Protein structure and function prediction
  • Variant effect prediction in genomics
  • Drug-target interaction prediction
  • Single-cell RNA-seq analysis workflows
  • Network biology and pathway analysis
Ethics & Responsible AI in Genomics
  • Bias in genomic datasets and population representation gaps
  • Reproducibility challenges in computational biology
  • Clinical misuse risks and responsible deployment
🏆 Capstone Presentation
  • Choose a biological ML problem of your interest
  • Build and train a model using bootcamp techniques
  • Evaluate performance and interpret results
  • Present findings to peers — Q&A and feedback session
  • Certificate distribution and closing remarks

All Code on GitHub

Every script, notebook, and dataset used in this bootcamp is available openly on GitHub. Fork the repo, follow along live, or revisit any session after the workshop ends.

  • 📓 Jupyter Notebooks for each day
  • 🧬 Sample FASTA and biological datasets
  • 🤖 Pretrained model usage examples
  • 📊 ML pipeline scripts with documentation
Open GitHub Repo ↗

🛠️ Tools & Requirements

All tools are free and open-source. Installation guides are provided on GitHub before the bootcamp begins.

🐍 Python 3.9+
🧬 Biopython
🤖 Scikit-learn
🔥 PyTorch
🧠 TensorFlow
📓 Jupyter Notebook
🐼 Pandas / NumPy
📊 Matplotlib

👥 Who Should Attend

✓ Undergraduate and postgraduate students in life sciences or bioinformatics
✓ PhD scholars requiring ML skills for genomics or proteomics research
✓ Faculty members exploring computational biology tools
✓ Researchers transitioning into computational or data-driven biology
✓ Industry professionals entering biotech or computational research

🎯 Learning Outcomes

✓ Write Python scripts for biological sequence analysis
✓ Use Biopython for parsing, transformation, and pipeline building
✓ Build and compare classical ML models on biological data
✓ Understand and implement neural networks for bioinformatics tasks
✓ Use pretrained protein and genomic language models (ESM, ProtBERT, DNABERT)
✓ Interpret AI models and understand responsible AI in genomics

✨ What's Included

🎥 Recorded Sessions — Access all 5 days of recordings anytime
📘 Comprehensive Study Materials — Complete documentation and guides
💻 GitHub Code Repository — All notebooks and scripts open access
🏅 Certificate of Completion — Official bootcamp certificate
🧬 Real Biological Datasets — Hands-on practice with authentic data
👨‍🏫 Expert Instructor Support — Direct access during and after sessions

⭐ Bootcamp Highlights

🎯 No prior ML experience required — beginner-friendly progression
💻 Hands-on coding every single day with real datasets
🧬 Covers the full AI pipeline: from raw sequences to pretrained LLMs
🐙 All code open-sourced on GitHub — yours to keep forever
👨‍🏫 Expert instructors with active bioinformatics research experience
🌍 Ethical AI in genomics — a topic rarely covered in similar workshops

🚀 Ready to Get Started?

Join the bootcamp and start building ML models on real biological data!

Completed GitHub Repo Contact Us