98%
921
2 minutes
20
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.ajodo.2023.06.012 | DOI Listing |
Emerg Top Life Sci
September 2025
Hurdle.bio / Chronomics Ltd., London, UK.
Artificial intelligence (AI) is transforming many fields, including healthcare and medicine. In biomarker discovery, AI algorithms have had a profound impact, thanks to their ability to derive insights from complex high-dimensional datasets and integrate multi-modal datatypes (such as omics, electronic health records, imaging or sensor and wearable data). However, despite the proliferation of AI-powered biomarkers, significant hurdles still remain in translating them to the clinic and driving adoption, including lack of population diversity, difficulties accessing harmonised data, costly and time-consuming clinical studies, evolving AI regulatory frameworks and absence of scalable diagnostic infrastructure.
View Article and Find Full Text PDFBioinformatics
September 2025
Department of Mathematical Sciences, The University of Texas at Dallas, TX United States.
Motivation: The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profiling while preserving spatial and morphological context. Understanding gene functions and interactions in different spatial domains is crucial, as it can enhance our comprehension of biological mechanisms, such as cancer-immune interactions and cell differentiation in various regions. It is necessary to cluster tissue regions into distinct spatial domains and identify discriminating genes that elucidate the clustering result, referred to as spatial domain-specific discriminating genes (DGs).
View Article and Find Full Text PDFAnn Surg Oncol
September 2025
Orthopaedic Oncology Service, Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, USA.
Background: Undifferentiated pleomorphic sarcoma (UPS) is a prevalent soft tissue sarcoma subtype associated with poor prognosis. Current prognostic tools lack the ability to incorporate personalized data for predicting survival. Machine learning (ML) offers a potential solution to enhance survival prediction accuracy.
View Article and Find Full Text PDFAm J Ophthalmol
September 2025
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Duke-NUS Graduate Medical School, Singapore; Department of Ophthalmology, Emory University School of Medicine, Emory University; Department of Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta
Purpose: To characterize the 3D structural phenotypes of the optic nerve head (ONH) in patients with glaucoma, high myopia, and concurrent high myopia and glaucoma, and to evaluate their variations across these conditions.
Design: Retrospective cross-sectional study.
Participants: A total of 685 optical coherence tomography (OCT) scans from 754 subjects of Singapore-Chinese ethnicity, including 256 healthy (H), 94 highly myopic (HM), 227 glaucomatous (G), and 108 highly myopic with glaucoma (HMG) cases METHODS: We segmented the retinal and connective tissue layers from OCT volumes and their boundary edges were converted into 3D point clouds.
PLoS One
September 2025
Smart Manufacturing and Artificial Intelligence, Micron Memory Malaysia Sdn. Bhd., Batu Kawan, Penang, Malaysia.
Advances in data collection have resulted in an exponential growth of high-dimensional microarray datasets for binary classification in bioinformatics and medical diagnostics. These datasets generally possess many features but relatively few samples, resulting in challenges associated with the "curse of dimensionality", such as feature redundancy and an elevated risk of overfitting. While traditional feature selection approaches, such as filter-based and wrapper-based approaches, can help to reduce dimensionality, they often struggle to capture feature interactions while adequately preserving model generalization.
View Article and Find Full Text PDF