mims-harvard/CLEF

Controllable Sequence Editing for Counterfactual Generation

38
/ 100
Emerging

This tool helps biologists and clinicians explore "what if" scenarios by precisely modifying existing sequences of biological or clinical events. You provide an initial patient or cell trajectory and a specific condition (e.g., a new treatment timing), and it generates a realistic predicted trajectory showing the likely outcome. Researchers and medical professionals can use this to understand disease progression, treatment impacts, or cellular reprogramming.

Use this if you need to generate realistic, hypothetical biological or clinical sequences with precise, localized changes to understand how specific interventions might alter a trajectory.

Not ideal if you need a general-purpose sequence generator without fine-grained control over when and where modifications occur, or if your data isn't sequential.

cellular-reprogramming patient-immune-dynamics treatment-planning disease-modeling biomedical-research
No License No Package No Dependents
Maintenance 10 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 15 / 25

How are scores calculated?

Stars

9

Forks

4

Language

Python

License

Last pushed

Mar 09, 2026

Commits (30d)

0

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