Deep Learning, Personalized Pharmacotherapy, Pregnancy, Psychiatric Disorders, Electronic Health Records, Drug Efficacy Share and Cite: Filippis, R. and Foysal, A. (2025) Deep Learning for ...

Computational models based on the integration of omics data have been used to identify synergistic combinations, but predicting drug synergy remains a challenge. Here, we introduce Drug synergy ...

Revised: This Reviewed Preprint has been revised by the authors in response ... incorporates drug-specific pathway activation scores (PASs) to estimate synergy scores as one of the key advancements ...

However, their effectiveness in addressing challenges related to drug ... prediction tasks, such as DDIs, still face many challenges. Existing methods typically rely on high-quality fine-tuning data ...

We trained machine learning algorithms that use clinical and ... used to compare the models were the AUROC for the response classification (the higher, the better) and the MSE for the ΔDAS28 ...

P-glycoprotein (P-gp) is regarded as an important factor in determining the ADMET (absorption, distribution, metabolism, elimination, and toxicity) characteristics of drugs ... prediction of P-gp ...

Chemotherapy is a vital treatment for people with all forms of cancer. It's a process that uses drugs designed to kill, ...

Imagine a bacterial cell—one of the multi-drug-resistant varieties that keep infectious disease experts up at night—blown ...

Researchers at VCU Massey Comprehensive Cancer Center have developed a novel algorithm that could provide a revolutionary ...

A research team from The University of Osaka and the Institute of Science Tokyo has developed a class of mRNA medicines that ...