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Welcome to the MethylNet wiki!

Levy, J. J., Titus, A. J., Petersen, C. L., Chen, Y., Salas, L. A., & Christensen, B. C. (2019). MethylNet: A Modular Deep Learning Approach to Methylation Prediction. BioRxiv, 692665. https://doi.org/10.1101/692665

This repository was designed to create a convenience tool for deep learning biological discovery in the methylation space, a tool that could be scaled to high-throughput workflows in the future.

The goal of this wiki is to build upon concepts introduced in https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess/wiki , and show how you can conduct the following deep learning tasks on methylation data:

1. Embeddings
2. Predictions (regression, classification)
3. Hyperparameter Optimization
4. Model Interpretation (Clustering of Embeddings, SHAP feature attributions)

The MethylNet API documentation and usage of all of its classes and functions can be found here: https://christensen-lab-dartmouth.github.io/MethylNet/

We're going to continue the analysis of our sample dataset (GSE87571) demonstrated in https://github.com/Christensen-Lab-Dartmouth/PyMethylProcess/wiki , so as a prerequisite please read through that wiki before continuing.