Published September 30, 2020 | Version 1.0.1

Convolutional Neural Net (CNN) models for ENCODE-Roadmap DNase-seq peaks and Transcription Factor ChIP-seq peaks - Basset architecture

  • 1. Stanford School of Medicine

Description

Deep learning models trained on epigenomic landscapes from ENCODE and Roadmap Epigenomics. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4059038. The file `nn.encode-roadmap.models.basset.clf.tar.gz` contains 10 cross-validated models in Tensorflow framework files as well as details on the architecture, cross-validation scheme, and training of these models. The file `nn.encode-roadmap.models.basset.clf.np_weights.tar.gz` contains the 10 cross-validated models' weights extracted to numpy array files (.npz).

Files

Files (7.8 GB)

Name Size
md5:a2ae78a1ef19741058ae896a1ff92b16
221.2 MB Download
md5:ab725b2acc03b4b8e8da0978e753c923
7.6 GB Download

Additional details

Related works

Is compiled by
Software: https://github.com/kundajelab/tronn (URL)
Is supplemented by
Dataset: 10.5281/zenodo.4059039 (DOI)