bilalmirza8519 / lstm-vae

Unsupervised Deep Learning for Temporal Multi-Omics

LSTM-VAE

Unsupervised Deep Learning for Multi-Omics

This is a keras code for LSTM-based variational autoencoder (LSTM-VAE). LSTM-VAE was employed to extract low-dimensional embeddings from time-series multi-omics data. The embeddings were fed to K-means clustering algorithm to group molecules based on their temporal patterns. Please refer to the figure LSTM-VAE.jpg

Please cite the following paper

Chung NC & Mirza B (joint first authors), Choi H, Wang J, Wang D, Ping P, Wang W. "Unsupervised Classification of Multi-Omics Data during Cardiac Remodeling using Deep Learning". Methods. 2019 Mar 7. https://doi.org/10.1016/j.ymeth.2019.03.004