Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data

被引：0
|
作者：
Zilin Ren
Quan Li
Kajia Cao
Marilyn M. Li
Yunyun Zhou
Kai Wang
机构：
[1] Children’s Hospital of Philadelphia,Raymond G. Perelman Center for Cellular and Molecular Therapeutics
[2] University Health Network,Princess Margaret Cancer Centre
[3] University of Toronto,Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine
[4] Children’s Hospital of Philadelphia,Department of Pathology and Laboratory Medicine, Perelman School of Medicine
[5] University of Pennsylvania,undefined
来源：
BMC Bioinformatics | / 24卷
关键词：
Generative adversarial networks; Variants annotation; Variants interpretation; Machine learning; Deep learning; Somatic variants;
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