Crmnet: a deep learning model for predicting gene expression from large regulatory sequence datasets

HIGHLIGHTS

• who: Ke Ding from the United States Chengdu of Information Technology United States have published the research: CRMnet: A deep learning model for predicting gene expression from large regulatory sequence datasets, in the Journal: (JOURNAL)
• what: The authors propose a novel DNN model (CRMnet) for predicting the expression levels of yeast promoter DNA sequences, which achieves a Pearson correlation coefficient of 0.971 in the test dataset, improving upon the benchmark models proposed in Vaishnav et_al . The authors demonstrate that the model can learn biologically meaningful information by quantifying the saliency information over known yeast . . .
  

   

  

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