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A Machine Learning Approach to Molecular Initiating Event Prediction Using High-Throughput Transcriptomic Chemical Screening Data.
Journal of chemical information and modeling
Jul 1, 2025
Improved scalability of high-throughput RNA-sequencing technologies has contributed to their proposed use in regulatory contexts for chemical hazard identification. However, the high dimensionality and size of these transcriptomic data sets present a...
Photoacoustic-Integrated Multimodal Approach for Colorectal Cancer Diagnosis.
ACS biomaterials science & engineering
Jul 1, 2025
Colorectal cancer remains a major global health challenge, emphasizing the need for advanced diagnostic tools that enable early and accurate detection. Photoacoustic (PA) spectroscopy, a hybrid technique combining optical absorption with acoustic res...
MoveMentor-examining the effectiveness of a machine learning and app-based digital assistant to increase physical activity in adults: protocol for a randomised controlled trial.
Trials
Jul 1, 2025
BACKGROUND: Physical inactivity is prevalent, leading to a high burden of disease and large healthcare costs. Thus, there is a need for affordable, effective and scalable interventions. However, interventions that are affordable and scalable are bese...
The General Attitudes towards Artificial Intelligence Scale (GAAIS): validation and psychometric properties analysis in the Italian context.
BMC psychology
Jul 1, 2025
This two-study investigation aimed to assess the psychometric properties of the Italian version of the General Attitudes towards Artificial Intelligence Scale (GAAIS). In study 1 (N = 236 adults) confirmatory factor analysis (CFA) was conducted to ex...
Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods.
Health and quality of life outcomes
Jul 1, 2025
BACKGROUND: Preference-based measures of health-related quality of life (HRQoL), such as the Short Form Six-Dimension (SF-6D) is essential for health economic evaluations. However, these measures are rarely included in clinical trials for lung cancer...
Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES.
BMC medical informatics and decision making
Jul 1, 2025
OBJECTIVE: Using 2005-2018 NHANES data, this study examined the association between the visceral fat metabolism score (METS-VF) and heart failure (HF) prevalence in U.S. adults, leveraging machine learning (LightGBM/XGBoost) and SHAP for classficatio...
Investigating psychotherapists' attitudes towards artificial intelligence in psychotherapy.
BMC psychology
Jul 1, 2025
BACKGROUND: The increasing prevalence of mental health disorders, compounded by a global shortage of psychotherapists, highlights the need for innovative solutions such as Artificial Intelligence (AI) or Machine Learning (ML) applications. These tech...
stGRL: spatial domain identification, denoising, and imputation algorithm for spatial transcriptome data based on multi-task graph contrastive representation learning.
BMC biology
Jul 1, 2025
BACKGROUND: Spatial transcriptomics now enables sequencing while preserving the spatial location of cells. This significantly enhances researchers' understanding of cellular and tissue functions in their spatial context. However, due to current techn...
RCFLA-YOLO: a deep learning-driven framework for the automated assessment of root canal filling quality in periapical radiographs.
BMC medical education
Jul 1, 2025
BACKGROUND: Evaluating the quality of root canal filling (RCF) performed by dental students in preclinical settings is a time-consuming process for clinicians and is often subjectively assessed.
Expert-augmented machine learning for predicting extubation readiness in the pediatric intensive care unit.
BMC medical informatics and decision making
Jul 1, 2025
BACKGROUND: Determining extubation readiness in pediatric intensive care units (PICU) is challenging. We used expert-augmented machine learning (EAML), a method that combines machine learning with human expert knowledge, to predict successful extubat...
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