EpiClass models
Collection
Classifier models trained on EpiATLAS dataset that were applied to ChIP-Atlas, ENCODE and recount3 datasets. (see paper)
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6 items
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Updated
Epigenomic Classifer - Assay
Epigenome Assay/Target classifier trained on the EpiATLAS dataset. The classes correspond to the minimal elements of the IHEC reference epigenome:
- Seven (7) ChIP: assays H3k27ac, H3k27me3, H3k36me3, H3k4me1, H3k4me3, H3k9me3, input
- RNA-Seq (two protocols, mrna/total)
- WGBS (two protocols, pbat/standard)
The model is a simple dense feedforward neural network, with one hidden layer of 3000 nodes. The model was trained using PyTorch Lightning. See Github repository labjacquespe/EpiClass for model code.
See the .o and .e files for training details. More information is also available on Comet ML, in the rabyj/epiclass project. The ID of this training run is 0f8e5eb996114868a17057bebe64f87c
For more context, see the associated publication: Leveraging a large harmonized epigenomic data collection for metadata prediction to validate and augment over 350,000 public epigenomic datasets
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