Generative deep learning models, such as those used for music generation, can produce a wide variety of results based on perturbations of random points in their latent space. User preferences can be incorporated in the generative process by replacing...
OBJECTIVE: To construct and validate a predictive model for the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures based on machine learning algorithms, so as to provide decision-making support for clinic...
Journal of controlled release : official journal of the Controlled Release Society
Nov 20, 2025
'Character is destiny' holds apt resonance in determining the biofate of nanocarriers, where the formation of biomolecular or protein corona (PC) around nanocarriers bestows a "new biological identity" different from their synthetic identity. The acq...
Artificial intelligence (AI) is rapidly reshaping neurology, offering opportunities to improve efficiency, expand access to care, and enhance clinical decision making. Yet, without deliberate safeguards, AI can perpetuate or exacerbate existing healt...
Journal of chemical information and modeling
Nov 20, 2025
Drug-induced osteotoxicity refers to the detrimental effects of certain drugs on bone metabolism, density, and structure, posing serious safety concerns in clinical practice, drug development, and environmental health. Although previous studies have ...
Drug-induced liver injury (DILI) is a leading cause of clinical trial attrition and postmarketing withdrawal and a major contributor to acute liver failure. As regulators increasingly encourage human-relevant, nonanimal approaches, accurate and inter...
Clinical heterogeneity among hemodialysis patients necessitates precision medicine approaches transcending conventional single-parameter management. Through machine learning analysis of 1,207 maintenance hemodialysis patients, we developed a novel tw...
Inferring gene regulatory networks (GRNs) is essential for understanding biological regulation. Although numerous deep learning approaches have been developed for GRN inference, most require large amounts of labeled data. We present Meta-TGLink, a st...
Animals can provide meaningful context for human single-cell data. To transfer information between species, we propose a deep learning approach that pre-trains a conditional variational autoencoder on animal data and transfers its final encoder layer...
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