M2 AI for Omics · 8 capsules
From Nucleus
to Neural Networks.
A grounded tour of transcriptomics for the people building the next generation of health AI.
The curriculum
Eight compact steps.
One connected story.
Each capsule is a short, focused bridge between what happens in a biological system and what appears in an AI pipeline.
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A working shelf
Resources for the
long way around.
The best transcriptomics work connects biological intuition, statistical discipline, and reproducible practice. These are the anchors used throughout the block.
Analysis of RNA-seq data
A practical companion for the sequencing and quantification pipeline.
↗ Paper · Nature MethodsSingle-cell RNA-seq
The foundational review for moving from averages to individual cells.
↗ Repository · ENCODEReference data in context
Explore how large-scale functional genomics data is organised and reused.
↗ Toolkit · scvi-toolsModels for single-cell data
Count-aware probabilistic models that take us beyond a two-dimensional plot.
↗About the instructor
Teaching the path from health data to intelligence.
Sandrine Muller, Eng. Msc., PhD is a Chaire de Professeur Junior and Tenure-track Assistant Professor leading the Health Data Integration and Exploitation team in AI for health.
Her work brings together biomedical data, reproducible analysis, and machine learning. In this course, she makes the assumptions between a physical molecule and an AI-ready representation visible.