Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
The emergence of next-generation sequencing (NGS) and high-throughput data has significantly enhanced biological research, enabling the study of genomes, gene expression, proteins, and metabolites at an unprecedented scale. However, the complexity and volume of this data pose challenges in terms of data processing, feature extraction, and pattern recognition. Machine learning and deep learning have become indispensable tools in bioinformatics to uncover hidden patterns and predict outcomes from this large-scale biological data. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The emergence of next-generation sequencing (NGS) and high-throughput data has significantly enhanced biological research, enabling the study of genomes, gene expression, proteins, and metabolites at an unprecedented scale. However, the complexity and volume of this data pose challenges in terms of data processing, feature extraction, and pattern recognition. Machine learning and deep learning have become indispensable tools in bioinformatics to uncover hidden patterns and predict outcomes from this large-scale biological data.
📋 Course Objectives
1. Apply bioinformatics 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 bioinformatics
• R&D engineers and working professionals applying bioinformatics in industry
• Academics and educators building research or teaching capacity in bioinformatics
🚀 Key Learning Outcomes
• A portfolio-grade bioinformatics deliverable you can defend and extend.
• 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
Biological Data as Machine Learning Input
• Encoding sequence, structure and expression for learning algorithms
• High-dimension low-sample-size regimes and regularisation strategy
• Splitting by homology or patient to avoid inflated performance
Established Methods That Still Win
• Regularised regression and tree ensembles on omics features
• Feature selection and the instability of selected gene panels
• Nested cross-validation for honest performance estimates
Architectures for Biological Data
• CNNs for genomic sequence and regulatory prediction
• Graph neural networks over molecular and interaction graphs
• Transformers and pretrained biological foundation models
Extracting Biology From a Model
• Attribution methods and in-silico mutagenesis
• Generating testable hypotheses rather than post-hoc narratives
• Distinguishing a learned biological signal from a dataset artefact
Rigour and Comparison
• Community benchmarks and the pitfalls of leaderboard chasing
• Baselines that must be reported for a claim to be credible
• Compute, reproducibility and releasing code that runs
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | CUDA |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Weights & Biases |
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.
