Artificial Intelligence in Biology – Self Learning

The syllabus covers

⦁ Introduction to AI and Its Relevance in Biology
⦁ Overview of AI, machine learning (ML), and deep learning.
⦁ Applications in biology: genomics, proteomics, drug discovery, and imaging.
⦁ Case studies: AI in protein folding (e.g., AlphaFold), disease prediction.
⦁ Reading: Review papers on AI in biology (e.g., Nature reviews).

⦁ Fundamentals of Machine Learning for Biology
⦁ Supervised vs. unsupervised learning, neural networks, decision trees.
⦁ Data preprocessing: handling biological datasets (e.g., genomic sequences, imaging data).
⦁ Tools: Python, R, TensorFlow, scikit-learn.
⦁ Practical: Install and explore Python libraries for ML.

⦁ AI in Genomics and Proteomics
⦁ AI for sequence analysis, variant calling, and gene expression.
⦁ Predictive modeling for protein structure and function.
⦁ Practical: Analyze a genomic dataset using a simple ML model (e.g., classification of gene variants).

⦁ AI in Drug Discovery and Systems Biology
⦁ AI in virtual screening, molecular property prediction, and network biology.
⦁ Tools: DeepChem, RDKit for cheminformatics.
⦁ Practical: Build a model to predict drug-target interactions.

⦁ Ethics, Challenges, and Future Directions
⦁ Bias in AI models, data privacy, and ethical considerations.
⦁ Limitations: data quality, interpretability, computational resources.
⦁ Future trends: AI in personalized medicine, synthetic biology.
⦁ Assignment: Write a short proposal on an AI application in biology.