Metadata-Version: 2.1
Name: dnngior
Version: 0.0.3
Summary: build high-quality genome-scale metabolic model by using a deep neural network to guide gapfilling
Author: Haris Zafeiropoulos
Author-email: haris.zafeiropoulos@kuleuven.be
Requires-Python: >=3.9,<3.12
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: cobra (<=0.23.0)
Requires-Dist: gurobipy (>=10.0.1,<11.0.0)
Requires-Dist: tensorflow (>=2.12.0,<3.0.0)
Description-Content-Type: text/markdown

# A novel way to gapfill metabolic models


## Description of the package

##Dependencies

- [Tensorflow](https://www.tensorflow.org/install) (or through [conda](https://anaconda.org/conda-forge/tensorflow))
- [cobrapy](https://opencobra.github.io/cobrapy/)

To run the "Going from a fasta file to a curated model" tutorial, that includes the reconstruction generation:

- [ModelSEEDp]y(https://github.com/ModelSEED/ModelSEEDpy)


## Installation instructions



# Examples of usage 

[how to use cobrapy and gapfill models](https://github.com/MGXlab/DNNGIOR/blob/main/files/examples/cobrapy.md)

<!-- pointing to the tutorials/NN_example.ipynb -->
[how to use NN-weights to gapfill models](https://colab.research.google.com/drive/1rNbFEUFEy_LoUhcp0R2aq3wrvqlcQAm4?usp=sharing#scrollTo=b66b7275)

<!-- pointing to the files/examples/fromFastaToModelSeedModell.py python script -->
[Going from a fasta file to a curated model](https://colab.research.google.com/drive/1gAnX3eGtyiGjVvt5rLiX7U2wnkM8JOF2#scrollTo=UfdQpuCAD-eR)



