https://github.com/greenelab/connectivity-search-analyses

hetnet connectivity search research notebooks (previously hetmech)
https://github.com/greenelab/connectivity-search-analyses

hetionet hetmat hetmech hetnet hetnet-connectivity-search matrix mechanism networks numpy scipy theory-in-practice

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hetnet connectivity search research notebooks (previously hetmech)

• Host: GitHub
• URL: https://github.com/greenelab/connectivity-search-analyses
• Owner: greenelab
• License: bsd-3-clause
• Created: 2016-07-21T21:25:53.000Z (over 8 years ago)
• Default Branch: main
• Last Pushed: 2023-03-30T12:27:01.000Z (over 1 year ago)
• Last Synced: 2024-05-02T06:00:22.595Z (7 months ago)
• Topics: hetionet, hetmat, hetmech, hetnet, hetnet-connectivity-search, matrix, mechanism, networks, numpy, scipy, theory-in-practice
• Language: Jupyter Notebook
• Homepage:
• Size: 51.3 MB
• Stars: 8
• Watchers: 8
• Forks: 5
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md
  • Citation: CITATION.cff

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README

        
# Hetnet connectivity search prototyping and data repository

[![Build Status](https://travis-ci.org/greenelab/connectivity-search-analyses.svg?branch=main)](https://travis-ci.org/greenelab/connectivity-search-analyses)

Connectivity Search (formerly called Hetmech for _hetnet mechanisms_) is a project to extract mechanistic connections between nodes in hetnets.

The project aims to identify the relevant network connections between query nodes.

The method is designed to operate on hetnets (networks with multiple node or relationship types).

**Note: the `hetmech` python package has been renamed to `hetmatpy` and relocated to [`hetio/hetmatpy`](https://github.com/hetio/hetmatpy).**

This repository is now used as a historical archive,

as well as a dataset storage, method prototyping, and exploratory data analysis repository.

Many findings from this repository are described in the [Connectivity Search Manuscript](https://greenelab.github.io/connectivity-search-manuscript/ "Hetnet connectivity search provides rapid insights into how two biomedical entities are related").

The manuscript source code is available in [`greenelab/connectivity-search-manuscript`](https://github.com/greenelab/connectivity-search-manuscript).

## Environment

This repository uses [conda](http://conda.pydata.org/docs/) to manage its environment as specified in [`environment.yml`](environment.yml).

Install the environment with:

```sh

# install new hetmech environment

conda env create --file=environment.yml

# update existing hetmech environment

conda env update --file=environment.yml

```

Then use `conda activate hetmech` and `conda deactivate` to activate or deactivate the environment.

Note that the environment is tested with the conda `channel_priority strict` configuration.

Locally, you can run the following commands to configure conda (as per https://conda-forge.org docs),

but note that it affects your conda config beyond this environment:

```shell

conda config --add channels conda-forge

conda config --set channel_priority strict

```

Another option is to install conda with [miniforge](https://github.com/conda-forge/miniforge).

## Acknowledgments

This work was supported through a research collaboration with [Pfizer Worldwide Research and Development](https://www.pfizer.com/partners/research-and-development).

This work was funded in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through Grants [GBMF4552](https://www.moore.org/grant-detail?grantId=GBMF4552) to Casey Greene and [GBMF4560](https://www.moore.org/grant-detail?grantId=GBMF4560) to Blair Sullivan.