Programme Parameters
Graduate / Intermediate
3 Days (4.5 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
Multi-omics data, which includes genomics, transcriptomics, proteomics, and metabolomics, provides a comprehensive view of biological systems but often presents challenges in integration due to the scale and complexity of the data. This course focuses on how AI, particularly machine learning and deep learning, can streamline the integration process, offering new methods for identifying biomarkers. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Multi-omics data, which includes genomics, transcriptomics, proteomics, and metabolomics, provides a comprehensive view of biological systems but often presents challenges in integration due to the scale and complexity of the data. This course focuses on how AI, particularly machine learning and deep learning, can streamline the integration process, offering new methods for identifying biomarkers.
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
👥 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
Biomarker Studies That Can Succeed
• Intended use: diagnostic, prognostic, predictive or monitoring
• Cohort design, sample size and the discovery-validation split
• Pre-analytical variability as the most common cause of false biomarkers
Combining Omic Layers
• Early, intermediate and late integration strategies
• Multi-omic factor analysis and joint dimensionality reduction
• Handling missing modalities across a cohort
Finding a Parsimonious Signature
• Feature selection stability across resamples
• Panel size against assay feasibility and cost
• Avoiding signatures that encode batch or site rather than biology
Evidence a Regulator Would Accept
• Independent cohort validation and prospective design
• Analytical validation of the eventual assay, not just the model
• Reporting standards and the reasons most published biomarkers fail
Toward Clinical Use
• Assay transfer from discovery platform to clinical format
• Health-economic case and clinical utility evidence
• Regulatory pathway for a companion or complementary diagnostic
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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