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Classroom Contents
Interpretable Machine Learning to Model Drug Perturbations in Single Cell Genomics
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- 1 Intro
- 2 The power of many
- 3 Single cell analysis for understanding cell fate in health & disease
- 4 Learning trajectories: cell cycle from morphometry
- 5 single-cell transcriptomies analysis
- 6 Machine learning based cell lineage estimation
- 7 cells as basis for understanding health
- 8 style transfer & domain adaptation by generative neural networks
- 9 scGen: predicting single-cell perturbation effects using generative models
- 10 Aim: interpretable and scalable perturbation modeling
- 11 Compositional perturbation autoencoder: training
- 12 Learning & predicting combinatorial genetic perturbations
