semvec/pypi-packages huggingface dataset:
krrood 2.0.5
A python package for Knowledge Representation and Reasoning systems based on Object Oriented Design.
License: MIT
Requires Python: >=3.10
Keywords: Knowledge-representation, reasoning, ontology, orm, query-langauge, database, python

# KRROOD
KRROOD stands for Knowlege Representation & Reasoning Through Object Oriented Design. This project aims to provide a
unified way to represent and reason about knowledge in Python using the principles of object oriented design.

This project also is consistent with many of the ideas mentioned in the book
[Knowledge Representation and Reasoning](https://www.oreilly.com/library/view/knowledge-representation-and/9781558609327/).
And of course the books about object oriented design.

## Installation

```bash
pip install krrood
```
If you want to use the visualization feature, you will also need to install [rustworkx_utils](https://github.com/AbdelrhmanBassiouny/rustworkx_utils).
```bash
pip install rustworkx_utils
```

## Entity Query Language (EQL)

EQL is a relational query language that is pythonic, and intuitive.

The interface side of EQL is inspired by [euROBIN](https://www.eurobin-project.eu/) entity query language white paper.

## Ormatic

## Documentation

Read the documentation [here](https://code-iai.github.io/krrood/intro.html).
benGRN 1.3.2
benchmarking gene regulatory networks
License: MIT
Requires Python: >=3.10
Keywords: GRN, benchmark, comp bio, gene regulatory networks, scPRINT, scRNAseq

# bengrn

[![codecov](https://codecov.io/gh/jkobject/benGRN/branch/main/graph/badge.svg?token=benGRN_token_here)](https://codecov.io/gh/jkobject/benGRN)
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[![Downloads](https://pepy.tech/badge/benGRN)](https://pepy.tech/project/benGRN)
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[![DOI](https://zenodo.org/badge/731249338.svg)](https://doi.org/10.5281/zenodo.10573209)

Benchmark your gene regulatory networks inference algorithm (from scRNAseq or bulk RNAseq dataset) with BenGRN


<img src="bengrn.png" width="400"/>

The package is supposed to work with [GRnnData](https://cantinilab.github.io/GRnnData/) and only uses biological ground truth datasets.

It can run Genie3 & pyscenic on your data as a comparison

It has 3 main different types of key ground truth data to compare your GRN to:

- Mc Calla et al.'s ChIP+Perturb ground truth
- omnipath's literature curated ground truth
- genome wide perturb seq 's dataset 

You can find the documentation [here](https://www.jkobject.com/benGRN/)

## Install it from PyPI

```bash
pip install bengrn
```

### Install it locally and run the notebooks:

```bash
git clone https://github.com/jkobject/benGRN.git
pip install -e benGRN
```

## Usage

```py
from bengrn import BenGRN
from bengrn import some_test_function

# a GRN in grnndata formart
grndata

BenGRN(grndata).do_tests()
#or
some_test_function(grndata)
```

see the notebooks in [docs](https://www.jkobject.com/benGRN/):

1. [omnipath](https://www.jkobject.com/benGRN/notebooks/bench_omni_genie3)
2. [genome wide perturb seq](https://www.jkobject.com/benGRN/notebooks/bench_perturbseq_genie3_transp/)
3. [Mc Calla](https://www.jkobject.com/benGRN/notebooks/bench_sroy_genie3_transp/)

## /!\ offline mode

If you want to run the notebooks offline, you need to download the data first.

```python
from bengrn import download_perturb_gt, download_GT_db, download_sroy_gt

download_perturb_gt()

```

## Development

Read the [CONTRIBUTING.md](CONTRIBUTING.md) file.

Awesome Benchmark of Gene Regulatory Networks created by @jkobject