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Course Detail

Course Name Artificial Intelligence for Biology
Course Code 25BIF314
Program B.Sc. (Hons.) Microbiology
Semester 6
Credits 3
Campus Amritapuri

Syllabus

Unit 1

Introduction to AI and Its Role in Biology – Definitions and scope of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). Types of machine learning: supervised, unsupervised, and reinforcement learning. Key concepts: features, labels, models, training, and validation. Overview of biological data types: genomic sequences, expression profiles, imaging, and structural data. Applications of AI in disease prediction, biomarker discovery, and pattern recognition in biological imaging, including disease marker detection, biodiversity conservation, and ecological monitoring such as pollution tracking.

Unit 2

AI Models for Molecular Biology – Role of AI models in protein structure prediction, functional annotation, and interaction prediction. Working with structure prediction data: reading PDB files, interpreting confidence scores, and understanding model limitations. Integration of model predictions into biological hypotheses. Introduction to prominent AI tools and models including AlphaFold, AlphaFold-Multimer, ESMFold, ESM3, RoseTTAFold, diffusion-based models, and FoldSeek.

Unit 3

Natural Language Processing and LLMs in Biology – Overview of NLP in biological contexts. Introduction to biomedical text databases: PubMed, PMC, and ClinicalTrials.gov. Application of NLP models in named entity recognition (genes, proteins, diseases), relation extraction (e.g., gene-disease, drug-target), automatic summarization, question answering, and literature-based discovery. Ethical considerations: misinformation and hallucination in LLMs, citation practices for AI-generated content, and training data bias.

Unit 4

Using AI Tools in Real-World Biological Research – Working with pre-trained models: interpretation of outputs, evaluation of model reliability and bias. Application of AI in drug discovery: virtual screening, target prediction, and drug repurposing. Ethical, legal, and societal aspects of AI in biology. Guidance on selecting appropriate AI tools based on research needs, accessibility, and ease of use.

Objectives and Outcomes

LEARNING OBJECTIVES:

An introductory course designed to familiarize students with the role of artificial intelligence in modern biological research. The course focuses on foundational AI concepts and their integration with molecular biology, biomedical literature mining, and real-world applications in drug discovery and protein structure prediction.

COURSE OUTCOMES:

After completing the course, students shall be able to

 

CO1. Understand the scope of artificial intelligence and its applications in biology.
CO2. Recognize and utilize AI models relevant to molecular biology, including protein structure prediction and function inference.
CO3. Employ natural language processing tools for biomedical literature mining and information extraction.
CO4. Evaluate pre-trained AI tools for use in drug discovery and biological data analysis.
CO5. Identify ethical and practical considerations while applying AI in biological research

Text Books / References

REFERENCES:

  1. Ramsundar, B., et al. Deep Learning for the Life Sciences, 2nd Edition. O’Reilly Media, 2019.
  2. A Biologist’s Guide to Artificial Intelligence, Springer, 2024.

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