Journal of chemical information and modeling
Oct 17, 2025
The generation and evaluation of chemical reactions remain challenging with limited comprehensive studies addressing these issues. We introduce the ical Reaction () ystematic ssessment of eneration and valuation () framework, an adaptable end-to-end ...
BACKGROUND: Accurate preoperative T and TNM staging of clear cell renal cell carcinoma (ccRCC) is crucial for diagnosis and treatment, but these assessments often depend on subjective radiologist judgment, leading to interobserver variability. This s...
Pediatric oral diseases affect over 60% of children globally, yet current diagnostic approaches lack precision and speed necessary for early intervention. This study developed a novel bio-inspired neutrosophic-enzyme intelligence framework integratin...
The burgeoning necessity for copious and diverse electrocardiogram (ECG) datasets for deep learning applications in clinical diagnostics has been impeded by the confidential nature of patient data. Related works have shown the effectiveness of additi...
In the context of the digital transformation of ideological and political education (IPE) in the new era, this study explores the interdisciplinary integration of red music and intelligent recommendation technologies. An intelligent deep learning mod...
On-demand access to information encoded in nucleotides lies at the heart of DNA/RNA applications. However, contemporary methods for targeted retrieval using PCR amplification or bead-based extraction, rely on Watson-Crick base pairing and pre-defined...
BACKGROUND: Emergency department (ED) crowding is often attributed to a slow hospitalization process, leading to reduced quality of care. Predicting early disposition in patients presenting with cardiac issues is challenging: most are ultimately disc...
Deep learning auto-segmentation has greatly advanced contouring in radiotherapy. However, quality assurance remains necessary due to performance fluctuation among individual patients. This manual process reintroduces variability and partially reduces...
Spatial transcriptomics is a rapidly developing field of single-cell genomics that quantitatively measures gene expression while providing spatial information within tissues. A key challenge in spatial transcriptomics is identifying spatially structu...
In health care, an accurate diagnosis with the help of a data-driven forecasting framework takes the risk factors associated with heart disease. However, building such an effective model using deep learning (DL) methods requires high-quality data, i....
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