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

HIGHLIGHTS

• who: Zilin Ren from the 9104, USA Centre, University Health have published the research: Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data, in the Journal: (JOURNAL) of March/31,/2021
• what: To address these limitations, in the current study, the authors developed a new semi-supervised generative adversarial neural_network (SGAN) method, which incorporated 12 clinical features of somatic variants and unlabeled variants. The study has three steps, including data preprocess, training semi-supervised learning model, and performance evaluation.
• how: Interpretability of the SGAN model . . .
  

   

  

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