# GenNet
Framework for Interpretable Neural Networks for Genetics
[GitHub](https://github.com/ArnovanHilten/GenNet)

# Model details
In [Just DNA-Seq](https://dna-seq.github.io/dna-seq/) analysis pipeline for personal and public genomes,
we use trained models from [GenNet paper](https://www.nature.com/articles/s42003-021-02622-z) for diseases
that can be associated with aging like diabetes, Atrial fibrillation (AF), hypertension, breast cancer, dementia, and balding.
The models were trained on UK Biobank data using 6,986,636 input variants from whole exome sequencing (WES) data.

# Model using
Both 'gennet_launch_*.py' and 'gennet_launch_*.ipynb' can be used to launch a model. 
For various types of architecture, files are complemented with appropriate masks.
Python 3.7 and TensorFlow 2.8.0 are needed, see full instructions on the GenNet GitHub page.


{"language": ["en"], "license": "apache-2.0"}
en license:apache-2.0 region:us
TAGS
#en #license-apache-2.0 #region-us 

# GenNet
Framework for Interpretable Neural Networks for Genetics
GitHub

# Model details
In Just DNA-Seq analysis pipeline for personal and public genomes,
we use trained models from GenNet paper for diseases
that can be associated with aging like diabetes, Atrial fibrillation (AF), hypertension, breast cancer, dementia, and balding.
The models were trained on UK Biobank data using 6,986,636 input variants from whole exome sequencing (WES) data.

# Model using
Both 'gennet_launch_*.py' and 'gennet_launch_*.ipynb' can be used to launch a model. 
For various types of architecture, files are complemented with appropriate masks.
Python 3.7 and TensorFlow 2.8.0 are needed, see full instructions on the GenNet GitHub page.


# GenNet
Framework for Interpretable Neural Networks for Genetics
GitHub # Model details
In Just DNA-Seq analysis pipeline for personal and public genomes,
we use trained models from GenNet paper for diseases
that can be associated with aging like diabetes, Atrial fibrillation (AF), hypertension, breast cancer, dementia, and balding.
The models were trained on UK Biobank data using 6,986,636 input variants from whole exome sequencing (WES) data. # Model using
Both 'gennet_launch_*.py' and 'gennet_launch_*.ipynb' can be used to launch a model. 
For various types of architecture, files are complemented with appropriate masks.
Python 3.7 and TensorFlow 2.8.0 are needed, see full instructions on the GenNet GitHub page.
TAGS
#en #license-apache-2.0 #region-us 
 # GenNet
Framework for Interpretable Neural Networks for Genetics
GitHub # Model details
In Just DNA-Seq analysis pipeline for personal and public genomes,
we use trained models from GenNet paper for diseases
that can be associated with aging like diabetes, Atrial fibrillation (AF), hypertension, breast cancer, dementia, and balding.
The models were trained on UK Biobank data using 6,986,636 input variants from whole exome sequencing (WES) data. # Model using
Both 'gennet_launch_*.py' and 'gennet_launch_*.ipynb' can be used to launch a model. 
For various types of architecture, files are complemented with appropriate masks.
Python 3.7 and TensorFlow 2.8.0 are needed, see full instructions on the GenNet GitHub page.