"This deals with a specific prediction task, namely to predict the expression of specified target genes from a panel of about 1,000 pre-selected “landmark genes”. As the authors explain, gene expression levels are often highly correlated and it may be a cost-effective strategy in some cases to use such panels and then computationally infer the expression of other genes. Based on Pylearn2/Theano."

https://github.com/uci-cbcl/D-GEX

https://followthedata.wordpress.com/2015/12/21/list-of-deep-learning-implementations-in-biology/