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AiGPro: a multi-tasks model for profiling of GPCRs for agonist and antagonist.
J Cheminform
January 2025
School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, 06978, Seoul, Republic of Korea.
G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening.
View Article and Find Full Text PDFAssessing the disconnect between student interest and education in artificial intelligence in medicine in Saudi Arabia.
BMC Med Educ
January 2025
Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.
Background: Although artificial intelligence (AI) has gained increasing attention for its potential future impact on clinical practice, medical education has struggled to stay ahead of the developing technology. The question of whether medical education is fully preparing trainees to adapt to potential changes from AI technology in clinical practice remains unanswered, and the influence of AI on medical students' career preferences remains unclear. Understanding the gap between students' interest in and knowledge of AI may help inform the medical curriculum structure.
View Article and Find Full Text PDFAI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.
BMC Pregnancy Childbirth
January 2025
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of Utah Health, 30 N. Mario Capecchi Dr., Level 5 South, Salt Lake City, UT, 84132, USA.
Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.
Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.
Resistance-breaking strains of tomato spotted wilt virus hamper photosynthesis and protein synthesis pathways in a virus accumulation-dependent manner in Sw5-carrying tomatoes.
Sci Rep
January 2025
Istituto per la Protezione Sostenibile delle Piante, Consiglio Nazionale delle Ricerche, via Amendola 165/A, 70126, Bari, Italy.
Tomato spotted wilt virus (TSWV; Orthotospovirus tomatomaculae) is one of the major horticultural threats due to its worldwide distribution and broad host range. In Italy, TSWV is widely spread in tomato (Solanum lycopersicum) crops and causes severe yield losses. In the last decades, several tomato varieties carrying the Sw-5b gene for resistance to TSWV have been released.
View Article and Find Full Text PDFIntricacies of human-AI interaction in dynamic decision-making for precision oncology.
Nat Commun
January 2025
Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progression for effective personalized care. However, AI's imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human-AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma.
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