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ModelSet

A file set grouping files that represent trained predictive models. Model file sets contain data files that could be used by predictive modeling software to generate predictions or annotations of genomic features such as genomic variants.

Properties

Name Type Description Notes
input_file_sets List[str] The file set(s) that served as inputs for the derivation of this model set. [optional]
release_timestamp str The date the object was released. [optional]
publication_identifiers List[str] The publication identifiers that provide more information about the object. [optional]
documents List[str] Documents that provide additional information (not data file). [optional]
lab str Lab associated with the submission. [optional]
award str Grant associated with the submission. [optional]
accession str A unique identifier to be used to reference the object prefixed with PKB. [optional]
alternate_accessions List[str] Accessions previously assigned to objects that have been merged with this object. [optional]
collections List[str] Some samples are part of particular data collections. [optional]
status str The status of the metadata object. [optional]
revoke_detail str Explanation of why an object was transitioned to the revoked status. [optional]
url str An external resource with additional information. [optional]
schema_version str The version of the JSON schema that the server uses to validate the object. [optional]
uuid str The unique identifier associated with every object. [optional]
notes str DACC internal notes. [optional]
aliases List[str] Lab specific identifiers to reference an object. [optional]
creation_timestamp str The date the object was created. [optional]
submitted_by str The user who submitted the object. [optional]
submitter_comment str Additional information specified by the submitter to be displayed as a comment on the portal. [optional]
description str A plain text description of the object. [optional]
dbxrefs List[str] Identifiers from external resources that may have 1-to-1 or 1-to-many relationships with IGVF file sets. [optional]
samples List[str] The sample(s) associated with this file set. [optional]
donors List[str] The donor(s) associated with this file set. [optional]
file_set_type str The category that best describes this predictive model set. [optional]
model_name str The custom lab name given to this predictive model set. [optional]
model_version str The semantic version number for this predictive model set. [optional]
prediction_objects List[str] The objects this predictive model set is targeting. [optional]
model_zoo_location str The link to the model on the Kipoi repository. [optional]
software_version str Version of software used for the derivation of this model set. [optional]
id str [optional]
type List[str] [optional]
summary str A summary of the object. [optional]
files List[str] The files associated with this file set. [optional]
control_for List[str] The file sets for which this file set is a control. [optional]
submitted_files_timestamp str The timestamp the first file object in the file_set or associated auxiliary sets was created. [optional]
input_file_set_for List[str] The file sets that use this file set as an input. [optional]

Example

from igvf_client.models.model_set import ModelSet

# TODO update the JSON string below
json = "{}"
# create an instance of ModelSet from a JSON string
model_set_instance = ModelSet.from_json(json)
# print the JSON string representation of the object
print(ModelSet.to_json())

# convert the object into a dict
model_set_dict = model_set_instance.to_dict()
# create an instance of ModelSet from a dict
model_set_from_dict = ModelSet.from_dict(model_set_dict)

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