openmedical/registry
← registry

candia

imported

software/candia

Canonical Decomposition of Data-Independent-Acquired Spectra

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

candia project image
GitHub preview card for fburic/candia. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
GPL-2.0(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
big-data · deconvolution · gpu · hpc · mass-spectrometry · parafac · parallel-factor-analysis · proteomics · tensor-decomposition
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.

  • Fast-Higashitensor-decomposition

    single-cell Hi-C, scHi-C, Hi-C, 3D genome, nuclear organization, tensor decomposition

  • holoscan-sdkgpu · hpc

    The AI sensor processing SDK for low latency streaming workflows

  • DeconvOptim.jldeconvolution · gpu

    A multi-dimensional, high performance deconvolution framework written in Julia Lang for CPUs and GPUs.

  • momomagickdeconvolution · gpu

    Python toolkit for 2D/3D image processing using CPU or GPU

  • KiwiMSdeconvolution · mass-spectrometry · proteomics

    Data analysis workflow for proteomics mass spectrometry featuring bayesian deconvolution and various downstream analyses.

  • bioinformatics-toolkithpc · proteomics

    Command-line tools for genomics and proteomics analysis

sources
  1. api.github.com/repos/fburic/candia
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2021-04-07, 7 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 GPL-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/61.json→ .entries["candia"]

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