📚 Syllabus & Course Curriculum
Bioinformatics & Computational Biology
Module-by-module breakdown of AI-Enabled Machine Learning Frameworks for Predictive Biomarker Identification, from foundations to a certified capstone project.
Definitions
• 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
Data
• 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
Selection
• 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
Evaluation
• 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
Translation
• 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
Earn government-registered certification in AI-Enabled Machine Learning Frameworks for Predictive Biomarker Identification
e-Certificate and e-Marksheet issued on successful completion.
