Course Description

*MODULE 1: Foundations of Bioinformatics**

1. **Introduction**
   - Central dogma of molecular biology
   - Applications and biological databases (NCBI, EMBL, DDBJ)

2. **Sequence Alignment & Analysis**
   - Types: Pairwise & Multiple
   - Tools: BLAST, Clustal Omega
   - Scoring matrices: PAM, BLOSUM

3. **Genomic Data Retrieval**
   - FASTA/GenBank formats
   - Gene prediction and functional annotation

4. **Protein Structure Prediction**
   - Primary to tertiary structure
   - Tools: PDB, SwissModel, Phyre2

5. **Omics Technologies**
   - Transcriptomics, Proteomics, Metabolomics
   - Basics of high-throughput sequencing

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**MODULE 2: Programming & Machine Learning**

6. **Python for Bioinformatics**
   - BioPython basics and biological data parsing

7. **AI & Machine Learning**
   - Supervised vs. Unsupervised learning
   - Algorithms: SVM, Random Forest, k-NN

8. **Data Cleaning & Feature Engineering**
   - Normalization, imputation, and feature extraction

9. **Applying ML to Biological Data**
   - Gene expression classification and disease prediction
   - Project Day 1: Sequence Classification Using ML

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**MODULE 3: Advanced AI Techniques**

10. **Deep Learning**
    - Basics of neural networks using Keras and TensorFlow

11. **Natural Language Processing in Genomics**
    - Treating DNA/RNA as language and embedding techniques

12. **CNNs for Structural Biology**
    - Image-based protein structure analysis

13. **AI in Drug Discovery**
    - Virtual screening and predictive modeling
    - Project Day 2: Gene Expression Clustering

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**MODULE 4: Applications & Projects**

14. **AI in Precision Medicine**
    - Personal genomics and treatment planning

15. **Ethics & Data Privacy**
    - HIPAA, GDPR, and bias in AI algorithms

16. **Bioinformatics in Vaccine & CRISPR Research**
    - Epitope prediction and sgRNA design

17. **Final Project**
    - Individual/group model development and presentation
    - Q&A on future scopes in bioinformatics and AI