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uniCATE

imported

software/unicate

Univariate conditional average treatment effect estimation for predictive biomarker discovery

Machine-generated from the listed sources and not yet reviewed by a human.

uniCATE project image
GitHub preview card for insightsengineering/uniCATE. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
Apache-2.0(osi)
Status
dormant
Maturity
deployed
Organization
insightsengineering
Country
unknown
Homepage
insightsengineering.github.io/uniCATE/
Repository
github.com/insightsengineering/uniCATE
Documentation
unknown
Tags
biomarkers · clinical-trials · high-dimensional-data · nonparametrics · r · treatment-effects
Regulatory
unknown
similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

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  • tmap2high-dimensional-data

    Tree-based visualization for high-dimensional data. Organizes similar items into interactive tree structures for any high-dimensional dataset.

  • ai4elifebiomarkers

    This data-centric AI repository implements a robust deep learning method (LFBNet) for fully automated tumor segmentation in whole-body [18]F-FDG PET/CT images.

  • An_ProtAIDe-Dxbiomarkers

    Proteomicis and AI for Dementia Diagnosis

  • frd-scorebiomarkers

    Official implementation of the Fréchet Radiomic Distance

  • hiwenetbiomarkers

    Histogram-weighted Networks for Connectivity & Advanced Analysis in Neuroscience

sources
  1. api.github.com/repos/insightsengineering/uniCATE
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2024-03-15, 4 stars, license reported as NOASSERTION. Category and schematic were assigned by keyword heuristics and are unreviewed. GitHub reported NOASSERTION; the licence was read from the LICENSE file as Apache-2.0, because GitHub's detector does not recognise open hardware licences.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/26.json→ .entries["unicate"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.