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https://github.com/opencog/agi-bio

Genomic and Proteomic data exploration and pattern mining
https://github.com/opencog/agi-bio

artificial-intelligence genetic-analysis genetics genomics protein-sequences proteomics

Last synced: 2 days ago
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Genomic and Proteomic data exploration and pattern mining

Host: GitHub
URL: https://github.com/opencog/agi-bio
Owner: opencog
License: other
Created: 2015-01-05T16:47:12.000Z (almost 10 years ago)
Default Branch: master
Last Pushed: 2022-07-30T23:23:44.000Z (over 2 years ago)
Last Synced: 2024-11-07T05:39:07.393Z (6 days ago)
Topics: artificial-intelligence, genetic-analysis, genetics, genomics, protein-sequences, proteomics
Language: Scheme
Homepage:
Size: 150 MB
Stars: 24
Watchers: 24
Forks: 30
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        AGI-Bio

=======

Genomic and proteomic research using the OpenCog toolset.

This includes experiments in applying MOSES, PLN, pattern mining

and other OpenCog components.

The [**MOZI.AI**](https://github.com/MOZI-AI) repositories make

use of this package, and extend the current development of OpenCog-based

bioinformatics tools as [**SingularityNET**](https://singularitynet.io/)

sevices.

Building and Installing

=======================

To build the AGI-Bio code, you will need to build and install the

[OpenCog AtomSpace](https://github.com/opencog/atomspace) first.

All of the pre-requistes listed there are sufficient to also build

this project. Building is as "usual":

```

    cd to project root dir

    mkdir build

    cd build

    cmake ..

    make -j

    sudo make install

    make -j test

```

Overview

========

The directory layout is as follows:

* **[bioscience](./bioscience)** - Provides the `GeneNode` and

  `MoleculeNode` Atom types.

* **[knowledge-import](./knowledge-import)** -- scripts for importing

   external knowledge bases into the AtomSpace.

* **[moses-scripts](./moses-scripts)** -- scripts for importing MOSES

  models; such models distinguish binary phenotype categories based

  on gene expression data.