Programme Parameters
Graduate / Intermediate
2 Days (3 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Genomic sequencing generates massive, context-rich strings of nucleotides. DNA-LLMs adapt the breakthroughs of language modeling—tokenization, context windows, attention—to capture regulatory grammar and long-range dependencies in DNA. When coupled with transfer learning and multi-task heads, these models enable accurate prediction of regulatory elements, variant effects, and non-coding function. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Genomic sequencing generates massive, context-rich strings of nucleotides. DNA-LLMs adapt the breakthroughs of language modeling—tokenization, context windows, attention—to capture regulatory grammar and long-range dependencies in DNA. When coupled with transfer learning and multi-task heads, these models enable accurate prediction of regulatory elements, variant effects, and non-coding function.
📋 Course Objectives
1. Apply biotechnology methods to authentic research and industry problems.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A demonstrable biotechnology project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Language Modelling Applied to DNA
• Why DNA is not language: no words, no sentences, weak compositional grammar
• Tokenisation choices — single nucleotide, k-mer, byte-pair — and their consequences
• Context window against genomic distance, and the enhancer problem it creates
The Current Landscape
• DNABERT, Nucleotide Transformer, HyenaDNA and Evo compared on context length
• Attention against state-space architectures for very long sequences
• Pretraining corpora and the species bias they carry
Regulatory and Functional Tasks
• Promoter, enhancer and splice site identification
• Chromatin accessibility and expression prediction, and Enformer as the reference point
• Non-coding variant effect prediction where laboratory data is scarce
Fine-Tuning and Interpretation
• Task heads, fine-tuning strategy and class imbalance in genomic labels
• Attention and attribution maps, and the weakness of reading biology from them
• In silico mutagenesis as a more defensible interpretation method
Whether the Model Is Actually Better
• Chromosome-level splits, since random splits leak through sequence homology
• Comparison against position weight matrices and CNN baselines
• Experimental validation such as MPRA before a regulatory claim is made
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | NLTK |
| Covered Tool / Platform | spaCy |
| Covered Tool / Platform | Hugging Face Transformers |
| Covered Tool / Platform | Gensim |
| Covered Tool / Platform | BERT |
| Covered Tool / Platform | GPT |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
