Cited by Interactive multi-hypergraph inferring and channel-enhanced and attribute-enhanced learning for drug-related side effect prediction

PregAN-NET: Addressing class imbalance with GANs in interpretable computational framework for predicting safety profile of drugs considering adverse reactions during pregnancy DOI

Anushka Chaurasia, Deepak Kumar,

Yogita Thakran

et al.

Journal of Biomedical Informatics, Journal Year: 2025, Volume and Issue: 166, P. 104832 - 104832

Published: April 28, 2025

Language: Английский

Citations

Characterization of adverse reactions to four common targeted drugs for hepatocellular carcinoma in WHO-VigiAccess DOI

Zhanshan Wang, Jiyao Sheng, Xuewen Zhang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 9, 2025

Language: Английский

Citations

Predicting the risk of drug-induced urticaria in patients with an allergic history using artificial neural networks DOI

Jose J. Álvarez Arroyo,

Horacio Rivera

International Journal of Research in Medical Sciences, Journal Year: 2025, Volume and Issue: 13(6), P. 2341 - 2345

Published: May 30, 2025

Background: Drug-induced urticaria is a frequent hypersensitivity reaction. Identifying individuals at risk crucial for clinical decision-making. Artificial neural networks (ANNs) offer promising approach to predicting adverse drug reactions in allergic patients. Methods: We conducted retrospective analysis using dataset of patients with known history. Various ANN architectures were trained and validated predict drug-induced based on demographic, clinical, pharmacological variables. Model performance was assessed accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC). Results: The model achieved high predictive outperforming traditional statistical methods. Key variables included previous reactions, type, comorbidities. demonstrated robust generalizability external validation. Conclusions: ANNs provide an effective tool Their implementation could enhance personalized medicine strategies improve patient safety. Further prospective studies are needed confirm these findings broader populations.

Language: Английский

Citations

Artificial intelligence in drug discovery and development: transforming challenges into opportunities DOI

Shashi Kant,

Deepika Deepika,

S. C. Roy

et al.

Published: June 2, 2025

Language: Английский

Citations

Interactive multi-hypergraph inferring and channel-enhanced and attribute-enhanced learning for drug-related side effect prediction DOI

Ping Xuan, S. Felix Wu, Hui Cui

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 184, P. 109321 - 109321

Published: Nov. 8, 2024

Language: Английский

Citations