Neural network based integration of assays to assess pathogenic potential

HIGHLIGHTS

  • who: Mohammed Eslami from the Bacteriology Reference Laboratory University have published the Article: Neural network based integration of assays to assess pathogenic potential, in the Journal: Scientific Reports Scientific Reports
  • what: Threat designations of the SBRL vectors based on literature and the models show consistent trends. The main contribution of this work was to show that inclusion of additional context from a prior dataset supplemented the models that were trained only on pathogenicity assays to assess pathogenic potential. After the model was trained, the 8-D flatten layer prior to the output layer was retrieved . . .

     

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