https://github.com/anbai106/magic

MAGIC: Multi-scAle heteroGeneity analysIs and Clustering

https://github.com/anbai106/magic

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 4 DOI reference(s) in README
  • Academic publication links
    Links to: sciencedirect.com
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (8.7%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

MAGIC: Multi-scAle heteroGeneity analysIs and Clustering

Basic Info
Statistics
  • Stars: 13
  • Watchers: 2
  • Forks: 2
  • Open Issues: 1
  • Releases: 0
Created about 6 years ago · Last pushed about 2 years ago
Metadata Files
Readme License

README.md

magic logo
MAGIC

Multi-scAle heteroGeneity analysIs and Clustering

Documentation

MAGIC

MAGIC, Multi-scAle heteroGeneity analysIs and Clustering, is a multi-scale semi-supervised clustering method that aims to derive robust clustering solutions across different scales for brain diseases.

:warning: The documentation of this software is currently under development

Citing this work

:warning: Please let me know if you use this package for your publication; I will update your papers in the section of Publication using MAGIC...

:warning: Please cite the software using the Cite this repository button on the right sidebar menu, as well as the original papers below ...

Original papers

Wen J., Varol E., Chand G., Sotiras A., Davatzikos C. (2020) MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_66

Wen J., Varol E., Chand G., Sotiras A., Davatzikos C. (2022) Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes. Medical Image Analysis, 2022. https://doi.org/10.1016/j.media.2021.102304 - Link

Owner

  • Name: Junhao (Hao) WEN
  • Login: anbai106
  • Kind: user
  • Location: NYC
  • Company: Columbia University

Medical Imaging Analysis, AI/ML, Multi-omics, Multi-organ

GitHub Events

Total
  • Watch event: 3
Last Year
  • Watch event: 3

Committers

Last synced: over 3 years ago

All Time
  • Total Commits: 19
  • Total Committers: 2
  • Avg Commits per committer: 9.5
  • Development Distribution Score (DDS): 0.105
Top Committers
Name Email Commits
anbai106 a****6@h****m 17
Junhao WEN j****9@g****m 2

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 0
  • Total pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 1
Past Year
  • Issues: 0
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 1
Top Authors
Issue Authors
Pull Request Authors
  • dependabot[bot] (2)
Top Labels
Issue Labels
Pull Request Labels
dependencies (2)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 14 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 3
  • Total maintainers: 1
pypi.org: magiccluster

Multi-scale semi-supervised clustering

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 14 Last month
Rankings
Dependent packages count: 6.6%
Stargazers count: 17.9%
Average: 23.6%
Forks count: 30.5%
Dependent repos count: 30.6%
Downloads: 32.5%
Maintainers (1)
Last synced: 11 months ago

Dependencies

requirements.txt pypi
  • nibabel *
  • numpy *
  • pandas *
  • scikit-learn ==0.21.3