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Abstract Artificial intelligence (AI) applications are having increasing impacts in the biomedical sciences. Modern AI tools enable uncovering hidden patterns in large datasets, forecasting outcomes, and numerous other a...

Abstract Background In biomedical research, subjects and biospecimens are commonly tracked using simple IDs or UUIDs, which guarantee uniqueness but convey no embedded semantic information. Contextual metadata (such as t...

Detection of atypical response trajectories in biomedical longitudinal databases Many health care professionals and institutions manage longitudinal databases, involving follow-ups for different patients over time. Longi...

Health and biomedical research are undergoing rapid advancement. New therapies, digital tools, and data-driven methods emerge on a daily basis, often accompanied by audacious claims and rapid shifts in attention. For rea...

This work includes materials aimed at training graduates and updating practising professionals in the framework of early intervention. It also integrates new biomedical engineering professionals. It contains information ...

Abstract Access to large, diverse biomedical datasets is critical for advancing medical research, yet privacy regulations severely restrict data sharing. We present an end-to-end framework for privacy-preserving health d...

Abstract Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems have significantly advanced data modelling capabilities and improved opportunities for extracting knowledge from vast and heterogeneo...

Small sample sizes in preclinical research limit the extraction of reliable knowledge and hinder translational progress. We propose genESOM, a generative artificial intelligence method based on emergent self‑organizing...

ABSTRACT Background and Aims Late diagnosis of chronic hepatitis B and C, often identified by the presence of advanced disease such as hepatocellular carcinoma or decompensated cirrhosis at the time of initial hepatitis ...

Malignant tumors pose a significant global health challenge. While established screening methods exist, they are largely site-specific, limiting comprehensive prevention. Multi-cancer screening, which detects multiple ca...

Abstract Quantum Machine Learning (QML) is emerging as a promising technology for tackling complex computational challenges, although its practical implementation faces significant obstacles owed to the inherent noise in...

Abstract Finding interesting phenomena is the core of scientific discovery, but the notion of interestingness is vaguely defined and heavily reliant on manual judgment. We present InterFeat, an integrative pipeline for a...

Incorporating polarization in computer vision tasks provides new solutions to high-level analytics, in particular when coupled with machine learning frameworks such as convolutional neural networks (CNN). A recent review...

This paper presents a review with the state-of-the-art of wireless microsystems for biomedical applications. Aspects including the radio-frequency systems, data acquisition, application specificities (especially those in...

Abstract Precise extraction of medical phenotypes and entities from electronic health record (EHR) text is vital for numerous clinical research tasks, including cohort classification, tracking temporal patterns in diseas...

Abstract Using biomedical foundation models (FMs) for inference on small cohorts, represents a promising and practical route to advance drug response biomarker discovery and target identification. Here, we demonstrate th...

Abstract Background Single-cell RNA sequencing (scRNA-seq) is considered a revolution in gene expression studies and offers significant benefits across various fields of biomedical or clinical applications. However, a hi...

Power analysis is a key component of planning prospective studies such as clinical trials. However, some journals in biomedical and psychosocial sciences request power analysis for data already collected and analysed bef...

Abstract Background There has been a recent push for biomedical research to incorporate more demographically, ethnically, and medically diverse cohorts – individuals whom the NIH designates as “underrepresented in bi...

The paper analyzes the existing non-invasive methods and tools for measuring and monitoring heart rate (HR), glucose saturation and human blood glucose level (BGL), shows ways of improving them to increase accuracy and e...

BackgroundYupingfeng Powder (YPF), a classical Chinese herbal formula composed of Astragalus membranaceus, Atractylodes macrocephala, and Saposhnikovia divaricata, has demonstrated potential efficacy in the treatment of ...

Abstract Detecting epileptic seizures from electroencephalogram (EEG) signals presents a significant challenge due to the nonlinear and non-stationary nature of brain activity. In this study, we propose a hybrid classifi...

Abstract Intertidal macrobenthos are vital bioindicators of coastal ecosystem health due to their ecological roles, limited mobility, and sensitivity to environmental disturbances. However, traditional field‐based moni...

Abstract This research employs hybrid machine learning techniques to optimize the mechanical properties of 3D-printed polylactic acid (PLA) lattice structures. By integrating random forest (RF) and XGBoost (XGB) algorith...