AIMC Topic: Machine Learning
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A machine learning model exploring creep performance of dental composites.
Dental materials : official publication of the Academy of Dental Materials
Jun 3, 2025
OBJECTIVES: Viscoelastic creep behaviour of RBCs determines their dimensional stability and thus contributes to their clinical performance. However, due to complex material compositional variations and differing testing protocols, comparing and analy...
Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning.
ACS nano
Jun 3, 2025
The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both and , leveraging physicochemical properties and e...
From Model Development to Mitigation: Machine Learning for Predicting and Minimizing Iodinated Trihalomethanes in Water Treatment.
Environmental science & technology
Jun 3, 2025
Disinfection processes in water treatment produce disinfection byproducts (DBPs), such as iodinated trihalomethanes (I-THMs), which pose significant health risks. Mitigating I-THMs remains challenging due to the complex interactions among water quali...
Construction and validation of a prognostic nomogram model integrating machine learning-pathomics and clinical features in IDH-wildtype glioblastoma.
Journal of translational medicine
Jun 3, 2025
BACKGROUND: Novel diagnostic criteria for glioblastoma (GBM) in the 2021 WHO classification emphasize the importance of integrating pathological and molecular features. Pathomics, which involves the extraction of digital pathology data, is gaining si...
Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis.
BMC oral health
Jun 3, 2025
OBJECTIVES: Accurate preoperative discrimination of salivary gland pleomorphic adenoma (SPA) stromal subtypes is essential for therapeutic plannings. We aimed to establish and test machine learning (ML) models for classification of stromal subtypes i...
Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning.
BMC medical informatics and decision making
Jun 3, 2025
BACKGROUND: Fatigue is a prevalent and debilitating symptom of non-communicable diseases (NCDs); however, its biological basis are not well-defined. This exploratory study aimed to identify key biological drivers of fatigue by integrating metabolomic...
Uncovering nonlinear patterns in time-sensitive prehospital breathing emergencies: an exploratory machine learning study.
BMC medical informatics and decision making
Jun 3, 2025
BACKGROUND: Timely prehospital care is crucial for patients presenting with high-risk time-sensitive (HRTS) conditions. However, the interplay between response time and demographic factors in patients with breathing problems remains insufficiently un...
Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume.
BMC psychiatry
Jun 3, 2025
BACKGROUND: The thalamus plays a crucial role in sensory processing, emotional regulation, and cognitive functions, and its dysregulation may be implicated in psychosis. The aim of the present study was to examine the differences in thalamic subregio...
Efficient structure learning of gene regulatory networks with Bayesian active learning.
BMC bioinformatics
Jun 3, 2025
BACKGROUND: Gene regulatory network modeling is a complex structure learning problem that involves both observational data analysis and experimental interventions. Bayesian causal discovery provides a principled framework for modeling observational d...
Identifying a gene signature for age-related hearing loss through machine learning and revealing the effect of the CTSS on the mice cochlea.
Biogerontology
Jun 3, 2025
Age-related hearing loss (ARHL) is one of the most common health conditions among the elderly population. This study used machine learning to screen for a gene signature to predicts ARHL. Four ARHL mice cochlear transcriptome datasets and the mRNA se...
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