PURPOSE: To investigate the effectiveness of an integrated deep-learning (DL) algorithm, the Mixture of Radiological Findings Specific Experts (MoRFSE), in breast cancer classification by imitating the diagnostic decision-making process of radiologis...
BACKGROUND: Asthma is a heterogeneous disease characterized by chronic airway inflammation and metabolic dysregulation. Recent studies highlight the role of glycolysis and oxidative phosphorylation (OXPHOS) imbalance in asthma pathogenesis, yet the u...
Achieving a deep understanding of brain mechanisms requires multi-scale perspectives to capture the architecture of complex networks. In this study, we focused on patients with cognitive impairment and constructed individual brain networks from neuro...
Early and accurate Alzheimer's disease (AD) diagnosis is critical for effective intervention, but it is still challenging due to neurodegeneration's slow and complex progression. Recent studies in brain imaging analysis have highlighted the crucial r...
Forensic DNA analysis is well established for phenotyping, providing valuable investigative leads. Proteomics, the large-scale study of proteins, is emerging as a complementary tool to DNA analysis, particularly for enhancing the evidential value of ...
BACKGROUND: Fresh embryo transfer reduces waiting time and minimizes embryo cryodamage for endometriosis (EM) patients. The current prediction models for fresh embryo transfer outcomes in EM primarily rely on logistic regression, with limited applica...
BMC medical informatics and decision making
Sep 3, 2025
BACKGROUND AND OBJECTIVES: Brain tissue oxygenation is usually inferred from arterial partial pressure of oxygen (paO), which is in turn often inferred from pulse oximetry measurements or other non-invasive proxies. Our aim was to evaluate the feasib...
This research aimed to develop a machine learning algorithm to predict suicide risk in bipolar disorder (BD) patients using RNA sequencing analysis of lymphoblastoid cell lines (LCLs). By identifying differentially expressed genes (DEGs) between high...
BACKGROUND: The aim of the study is to develop a machine learning (ML) model to identify contributing factors to dementia-related safety events using patient safety event report data.
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