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type: pytorch
args:
module_file: pretrained_model_reloaded_th.py
module_obj: model
weights:
md5: 4878981d84499eb575abd0f3b45570d3
url: https://zenodo.org/record/1466068/files/pretrained_model_reloaded_th.pth?download=1
default_dataloader:
defined_as: kipoiseq.dataloaders.SeqIntervalDl
default_args:
alphabet_axis: 0
auto_resize_len: 600
dtype: np.float32
dummy_axis: 2
dependencies:
conda:
- python=3.6
- h5py=2.10.0
- _pytorch_select=0.2=gpu_0
- pytorch=1.3.1=cuda100py36h53c1284_0
- pip=20.3.3
- pysam=0.15.3
- cython=0.29.23
pip:
- kipoiseq
info:
authors:
- github: davek44
name: David R. Kelley
cite_as: https://doi.org/10.1101/gr.200535.115
contributors:
- github: krrome
name: Roman Kreuzhuber
trained_on: "From 2,071,886 total sites, 71,886 randomly reserved for testing and 70,000 for validation, leaving 1,930,000 for training."
doc: "This is the Basset model published by David Kelley converted to pytorch by\
\ Roman Kreuzhuber. It categorically predicts probabilities of accesible genomic\
\ regions in 164 cell types (ENCODE project and Roadmap Epigenomics Consortium). Data was generated using DNAse-seq. The sequence\
\ length the model uses as input is 600bp. The input of the tensor has to be (N,\
\ 4, 600, 1) for N samples, 600bp window size and 4 nucleotides. Per sample, 164\
\ probabilities of accessible chromatin will be predicted. \n"
license: MIT
name: Basset
tags:
- DNA accessibility
version: 0.1.0
schema:
inputs:
associated_metadata: ranges
doc: DNA sequence
name: seq
shape: (4,600,1)
special_type: DNASeq
targets:
column_labels:
- target_labels.txt
doc: Probability of accessible chromatin in 164 cell types
name: DHS_probs
shape: (164, )
test:
expect:
url: https://s3.eu-central-1.amazonaws.com/kipoi-models/predictions/14f9bf4b49e21c7b31e8f6d6b9fc69ed88e25f43/Basset/predictions.h5
md5: 9df59f9899b27e65ab95426cb9557ad3 |