{"id":17131826,"url":"https://github.com/raamana/hiwenet","last_synced_at":"2025-04-13T07:55:36.590Z","repository":{"id":57437401,"uuid":"98179864","full_name":"raamana/hiwenet","owner":"raamana","description":"Histogram-weighted Networks for Connectivity \u0026 Advanced Analysis in Neuroscience","archived":false,"fork":false,"pushed_at":"2022-07-14T03:14:13.000Z","size":1131,"stargazers_count":8,"open_issues_count":1,"forks_count":2,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-11T12:11:40.647Z","etag":null,"topics":["biomarkers","connectivity","feature-extraction","graph","histogram-weighted-networks","machine-learning","neuroimaging","neuroscience"],"latest_commit_sha":null,"homepage":"http://hiwenet.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/raamana.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-07-24T10:41:30.000Z","updated_at":"2024-08-19T12:35:13.000Z","dependencies_parsed_at":"2022-09-11T02:51:05.840Z","dependency_job_id":null,"html_url":"https://github.com/raamana/hiwenet","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raamana%2Fhiwenet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raamana%2Fhiwenet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raamana%2Fhiwenet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/raamana%2Fhiwenet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/raamana","download_url":"https://codeload.github.com/raamana/hiwenet/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248602364,"owners_count":21131625,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["biomarkers","connectivity","feature-extraction","graph","histogram-weighted-networks","machine-learning","neuroimaging","neuroscience"],"created_at":"2024-10-14T19:25:01.008Z","updated_at":"2025-04-13T07:55:36.563Z","avatar_url":"https://github.com/raamana.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Histogram-weighted Networks (hiwenet)\n\n[![status](http://joss.theoj.org/papers/df10a3a527fe169447a64c0cc810ff3c/status.svg)](http://joss.theoj.org/papers/df10a3a527fe169447a64c0cc810ff3c)\n[![travis](https://travis-ci.org/raamana/hiwenet.svg?branch=master)](https://travis-ci.org/raamana/hiwenet.svg?branch=master)\n[![Code Health](https://landscape.io/github/raamana/hiwenet/master/landscape.svg?style=flat)](https://landscape.io/github/raamana/hiwenet/master)\n[![codecov](https://codecov.io/gh/raamana/hiwenet/branch/master/graph/badge.svg)](https://codecov.io/gh/raamana/hiwenet)\n[![PyPI version](https://badge.fury.io/py/hiwenet.svg)](https://badge.fury.io/py/hiwenet)\n[![Python versions](https://img.shields.io/badge/python-2.7%2C%203.5%2C%203.6-blue.svg)]\n\nHistogram-weighted Networks for Feature Extraction and Advanced Analysis in Neuroscience\n\nNetwork-level analysis of various features, esp. if it can be individualized for a single-subject,\n 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.\n\nRough scheme of computation is shown below:\n![illustration](docs/illustration.png)\n\n## Installation\n\n`pip install -U hiwenet`\n\n## Documentation\n\n|||\n|--:|---|\n| Docs: |  http://hiwenet.readthedocs.io |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraamana%2Fhiwenet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fraamana%2Fhiwenet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fraamana%2Fhiwenet/lists"}