β Authors:
BEL Alexis - BELAKTIB Anas - OUSSAREN Mohamed - ROUAUD Lucas
Master 2 Bio-informatics at UniveritΓ© de Paris.
- π Report:
doc/report/report.pdf
- π’ Oral presentation:
doc/presentation
- π₯ Main:
src/main.py
This project is actually trying to answer to this Kaggle project: OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction.
The main problematic is the synthesis of a stable mRNA vaccine. Because of the molecular nature of it, the vaccine degraded itself easily and quickly. To counter that, it is necessary to synthesize a mRNA stable.
To do so, the product mRNA have to be tested. And this is where this project take place: creating a neural network to predict the stability of a given sequence.
Because this project is done to validate a course, one mandatory criterion is create two neural network approches. Here, we've done:
- Three different embedding:
- One from
keras
. - One recode by ourselves.
- One using
RNABERT
transformer.
- One from
- Two neural networks:
- One Convutional Neural Network (CNN).
- One Google Inception.
To use this program, you will need to create a conda environment like so:
mamba env create --file kaggle_reseau.yml
conda env create --file kaggle_reseau.yaml
conda activate reseau
To launch this program, simply use the next commands (after the activation of the conda environment):
python3 src/main.py --help
Next, the parameters are described:
Parameters | Parameters name | Usage |
---|---|---|
*-i, --input |
Input X + Y data/neural network trained |
Add an .npy data file or a .h5 neural network file. |
*-o, --output |
Output data Y /neural network finish to be trained. |
Add an .npy data file or a .h5 neural network file. |
-pred, --predict_data |
Output predicted Y data. |
Add an .npy data file. |
--cnn |
Convolutional Neuronal Network. | Add like a True . |
--ginc, --google_inception |
Google inception's neural network. | Add like a True . |
--ke, --keras_embedding |
Using classical keras embedding method. |
Add like a True . |
--owe, --own_embedding |
Using our compute pre-embedding. | Add like a True . |
--re, --rnabert_embedding |
Using embedding compute by RNABERT transformer. |
Add like a True . |
You actually said to the program that you want to train neural network. To do that, give to -i, --input
a dataset to learn and to -o, --output
the neural network to reuse. Do not forget to indicate a type of neural network (-ginc, --google_inception
or -ke, --keras_embedding
) to use and a type of an input embedding (-ke, --keras_embedding
, -hme, --homemade_embedding
or -re, --rnabert_embedding
).
You actually said to the program that you already have a train neural network. So you want to predict Y
data base on X
data. To do that, give to -i, --input
a trained neural network, to -o, --output
how to write the Y
predict data and to -pred, --predict_data
the X
data. Do not forget to indicate the good input embedding (-ke, --keras_embedding
, -hme, --homemade_embedding
or -re, --rnabert_embedding
).