Deep learning in epigenomics and the new Python tool MethylNet
The use of deep learning in analyses of DNA methylation data is beginning to emerge and distill non-linear relationships among high-dimensional data features. However, a generalized and user-friendly approach for execution, training, and interpreting models for methylation data is lacking.
We introduce and demonstrate the robust performance of MethylNet on downstream tasks of DNA methylation analysis, including cell-type deconvolution, pan-cancer classification, and subject age prediction. We interrogate the learned features from a pan-cancer classification to show high fidelity clustering of cancer
Our findings demonstrate
Joshua J. Levy, Alexander J. Titus, Curtis L. Petersen, Youdinghuan Chen, Lucas A. Salas, Brock C. Christensen (2019) MethylNet: A Modular Deep Learning Approach to Methylation Prediction. bioRxiv. [DOI: 10.1101/692665] [PDF].