RNA-to-Protein Prediction Using Machine Learning and Deep Learning Models

Project Overview This project focuses on predicting protein expression from RNA data using a variety of machine learning (ML) and deep learning (DL) models. The models aim to map gene expression levels to corresponding protein levels by training on single-cell multiomics data. The dataset is taken from https://www.kaggle.com/competitions/open-problems-multimodal/overview

Requirements

python, numpy, pandas, pytables, h5py, tables, pytorch

To run scgpt model https://github.com/bowang-lab/scGPT/tree/f6097112fe5175cd4e221890ed2e2b1815f54010 this needs to be cloned it is the original scgpt model it also cotains the pretrained weights.

Training

To train the models, run the following commands in the terminal:

Training the Ridge Regression Model:

python Ridge_Regression.py

Training the Random Forest Model:

python Random_Forest.py

Training the Fully Connected Neural Network (FCNN):

python Neural_network.py

Training the Autoencoder-Based Model:

python Encoder_Decoder.py

Training the SCGPT + Neural Network Model:

python rnatoprotein.py