Histogram-weighted Networks for Feature Extraction, Connectivity and Advanced Analysis in Neuroscience
Histogram-weighted Networks for Feature Extraction, Connectivity and Advanced Analysis in Neuroscience - Published in JOSS (2017)
Science Score: 93.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org -
○Committers with academic emails
-
○Institutional organization owner
-
✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
Histogram-weighted Networks for Connectivity & Advanced Analysis in Neuroscience
Basic Info
- Host: GitHub
- Owner: raamana
- License: mit
- Language: Python
- Default Branch: master
- Homepage: http://hiwenet.readthedocs.io
- Size: 1.08 MB
Statistics
- Stars: 8
- Watchers: 2
- Forks: 3
- Open Issues: 1
- Releases: 5
Topics
Metadata Files
README.md
Histogram-weighted Networks (hiwenet)
[
]
Histogram-weighted Networks for Feature Extraction and Advanced Analysis in Neuroscience
Network-level analysis of various features, esp. if it can be individualized for a single-subject, is proving to be a valuable tool in many applications. Ability to extract the networks for a given subject individually on its own, would allow for feature extraction conducive to predictive modeling, unlike group-wise networks which can only be used for descriptive and explanatory purposes. This package extracts single-subject (individualized, or intrinsic) networks from node-wise data by computing the edge weights based on histogram distance between the distributions of values within each node. Individual nodes could be an ROI or a patch or a cube, or any other unit of relevance in your application. This is a great way to take advantage of the full distribution of values available within each node, relative to the simpler use of averages (or another summary statistic) to compare two nodes/ROIs within a given subject.
Rough scheme of computation is shown below:
Installation
pip install -U hiwenet
Documentation
||| |--:|---| | Docs: | http://hiwenet.readthedocs.io |
Owner
- Name: Pradeep Reddy Raamana
- Login: raamana
- Kind: user
- Location: Pittsburgh, PA
- Company: University of Pittsburgh
- Website: crossinvalidation.com
- Twitter: Raamana_
- Repositories: 114
- Profile: https://github.com/raamana
Neuroscientist trying to bridge the gap between clinic & computer science. Interests: Machine learning, Neuroimaging, Brain disorders, Informatics, Open science
JOSS Publication
Histogram-weighted Networks for Feature Extraction, Connectivity and Advanced Analysis in Neuroscience
Authors
Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
Editor
Christopher R. Madan
Tags
connectivity neuroscience graph histogram machine-learningGitHub Events
Total
- Fork event: 1
Last Year
- Fork event: 1
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Pradeep Reddy Raamana | r****a@g****m | 135 |
| Pradeep Reddy Raamana | p****a@r****g | 40 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 11
- Total pull requests: 1
- Average time to close issues: about 1 month
- Average time to close pull requests: about 1 hour
- Total issue authors: 2
- Total pull request authors: 1
- Average comments per issue: 5.82
- Average comments per pull request: 1.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- oesteban (6)
- raamana (5)
Pull Request Authors
- raamana (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 26 last-month
- Total dependent packages: 1
- Total dependent repositories: 1
- Total versions: 7
- Total maintainers: 1
pypi.org: hiwenet
Histogram-weighted Networks for Feature Extraction and Advance Analysis in Neuroscience
- Homepage: https://github.com/raamana/hiwenet
- Documentation: https://hiwenet.readthedocs.io/
- License: mit
-
Latest release: 0.4.5
published over 3 years ago
Rankings
Maintainers (1)
Dependencies
- hypothesis *
- matplotlib *
- networkx *
- numpy *
- numpydoc *
- sphinx-argparse *
- codecov *
- hypothesis *
- matplotlib *
- medpy *
- networkx *
- numpy *
- medpy *
- networkx *
- nibabel *
- numpy *
- pyradigm *