4P Predicting triple-negative breast cancer molecular subtype from hematoxylin and eosin using deep neural networks

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the precision-recall curve (AUPR).Predictions were generated for PC cell lines following bulk cell line RNA sequencing.26 compounds predicted to be effective in PC cell lines were tested in vitro and found to inhibit growth in at least one cell line.Table: 3O Performance summary for finalized deep neural network models Model Loss Accuracy (%) AUROC (%) AUPR (%) Precision (%) Recall (%) Specificity (%) Drug sensitivity MLP 0.1917 92.66 95.06 83.04 75.28 75.76 95.63 Drug sensitivity 2D-CNN 0.1963 92.26 95.35 82.33 71.18 80.98 94.24Conclusions: The study results showed that pan-cancer deep neural network models leveraging gene expression input data are capable of accurately predicting drug sensitivity for individual cancer cell lines.A deep learning model trained on pancancer data was able to accurately identify existing drugs with preclinical efficacy in prostate cancer.Legal entity responsible for the study: The authors.

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