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
Real-World Applications Apply Cancer Risk Prediction with Machine Learning for Bioinformatics skills directly to academic research, thesis work, and publications. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Real-World Applications
Apply Cancer Risk Prediction with Machine Learning for Bioinformatics skills directly to academic research, thesis work, and publications
📋 Course Objectives
1. Translate bioinformatics theory into practical, reproducible analysis.
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 demonstrable bioinformatics 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 a Risk Model Is For
• Absolute against relative risk and what a clinician can act on
• Screening, surveillance and prevention as different decision contexts
• Existing models such as Gail, Tyrer-Cuzick and BOADICEA as the baseline to beat
Inputs and Their Biases
• Germline variants, polygenic risk scores, clinical and lifestyle variables
• Ancestry bias in GWAS-derived scores and the poor transfer across populations
• Case-control sampling and how it distorts an apparent risk estimate
Building the Predictor
• Survival models against binary classification, and the censoring that decides it
• Regularised regression and gradient boosting on tabular clinical data
• Leakage through follow-up variables that encode the outcome
Testing Honestly
• Discrimination, calibration and the recalibration a transferred model needs
• Decision curve analysis against current screening guidelines
• External validation in an independent cohort as the minimum credible evidence
Deployment and Consequences
• Incidental findings, genetic counselling and the duty to inform
• Insurance, discrimination and the legal position in different jurisdictions
• Overdiagnosis and the harm a well-calibrated model can still cause
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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