Programme Parameters
Foundation
6 Weeks (60 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
Biomarkers play a critical role in modern healthcare, enabling early disease detection, patient stratification, and personalized treatment strategies. With the rise of high-throughput technologies such as genomics, transcriptomics, proteomics, and clinical datasets, identifying reliable biomarkers requires advanced computational approaches. Traditional statistical methods often fall short when dealing with high-dimensional data, making machine learning (ML) essential for extracting meaningful patterns. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Biomarkers play a critical role in modern healthcare, enabling early disease detection, patient stratification, and personalized treatment strategies. With the rise of high-throughput technologies such as genomics, transcriptomics, proteomics, and clinical datasets, identifying reliable biomarkers requires advanced computational approaches. Traditional statistical methods often fall short when dealing with high-dimensional data, making machine learning (ML) essential for extracting meaningful patterns.
📋 Course Objectives
1. Translate biotechnology theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 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
What Kind of Biomarker Is Being Sought
• Prognostic, predictive, diagnostic and pharmacodynamic biomarkers distinguished
• BEST framework terminology and the context of use that governs validation
• Intended clinical decision defined before any data analysis begins
High-Dimensional Sources and Their Traps
• Genomic, transcriptomic, proteomic and clinical data combined in one matrix
• Missingness that is not at random, and imputation that hides it
• Batch and site effects confounded with outcome — the classic false discovery
Finding Candidate Features
• Univariate filters, LASSO and elastic net, and stability selection over resamples
• Correlated features and the instability of any single selected panel
• Nested cross-validation as the only unbiased estimate of performance
Measuring Clinical Usefulness
• Discrimination with AUC against calibration, which is more often reported badly
• Decision curve analysis and net benefit over the existing standard of care
• Class imbalance and why accuracy is meaningless for a rare outcome
Validation and Reporting
• Internal, temporal and external validation and what each actually demonstrates
• TRIPOD+AI and REMARK reporting checklists
• Assay reproducibility, cost and turnaround as the real barriers to adoption
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | XGBoost |
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