mims-harvard/CLEF
Controllable Sequence Editing for Counterfactual Generation
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.
Stars
9
Forks
4
Language
Python
License
—
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
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curl "https://pt-edge.onrender.com/api/v1/quality/generative-ai/mims-harvard/CLEF"
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