Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings

Abstract Background Current approaches to identifying drug-drug Ornament interactions (DDIs), include safety studies during drug development and post-marketing surveillance after approval, offer important opportunities to identify potential safety issues, but are unable to provide complete set of all possible DDIs.Thus, the drug discovery researche

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Structural Health Monitoring of Underground Metro Tunnel by Identifying Damage Using ANN Deep Learning Auto-Encoder

Due to the complexity of underground environmental conditions and operational Trinket Tray incidents, advanced and accurate monitoring of the underground metro shield tunnel structures is crucial for maintenance and the prevention of mishaps.In the past few decades, numerous deep learning-based damage identification studies have been conducted on a

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