A deep learning models to predict silencers and enhancers in the DLPFCs

We developed a deep learning framework combining bulk and single-cell epigenomic data to evaluate noncoding AD variants' regulatory potential (i.e., silencing and activating strength) in the dorsolateral prefrontal cortex (DLPFC) and its major cell types. The AZ.tar.gz package includes a) all data used for training, b) the trained model, c) the programs as needed, d) example datasets for using the programs. The alternativeModels.tar.gz package includes a) CNNtran** files are for model A; b) CNNlarge** files are for model B; c) CNNsmall** files are for model C. **_single_model.hdf5 is the file for the structure of a model. **_model_weights.hdf5 is the file for the weights of a trained model.

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