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http://dx.doi.org/10.1016/j.ajem.2005.09.001 | DOI Listing |
Gigascience
January 2025
Helmholtz AI, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany.
Background: The ability to differentiate between viable and dead microorganisms in metagenomic data is crucial for various microbial inferences, ranging from assessing ecosystem functions of environmental microbiomes to inferring the virulence of potential pathogens from metagenomic analysis. Established viability-resolved genomic approaches are labor-intensive as well as biased and lacking in sensitivity.
Results: We here introduce a new fully computational framework that leverages nanopore sequencing technology to assess microbial viability directly from freely available nanopore signal data.
BMC Bioinformatics
September 2025
Computational Chemical Biology Laboratory, Department of BioMolecular Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, 14040-900, Brazil.
Antimicrobial resistance (AMR) is one of the most concerning modern threats as it places a greater burden on health systems than HIV and malaria combined. Current surveillance strategies for tracking antimicrobial resistance (AMR) rely on genomic comparisons and depend on sequence alignment with strict similarity cutoffs of greater than 95%. Therefore, these methods have high false-negative error rates due to a lack of reference sequences with a representative coverage of AMR protein diversity.
View Article and Find Full Text PDFPLoS One
August 2025
Department of Civil and Architectural Engineering, University of Miami, Coral Gables, Florida, United States of America.
Manual diagnosis of hematological cancers like leukemia through bone marrow smear analysis is labor-intensive, prone to errors, and highly dependent on expert knowledge. To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.
View Article and Find Full Text PDFDiagnostics (Basel)
August 2025
Computer Engineering Department, Engineering and Architecture Faculty, Eskisehir Osmangazi University, Eskisehir 26040, Türkiye.
: Clinicians routinely rely on periapical radiographs to identify root-end disease, but interpretation errors and inconsistent readings compromise diagnostic accuracy. We, therefore, developed an explainable, multimodal AI framework that (i) fuses two data modalities, deep CNN embeddings and radiomic texture descriptors that are extracted only from lesion-relevant pixels selected by Grad-CAM, and (ii) makes every prediction transparent through dual-layer explainability (pixel-level Grad-CAM heatmaps + feature-level SHAP values). : A dataset of 2285 periapical radiographs was processed using six CNN architectures (EfficientNet-B1/B4/V2M/V2S, ResNet-50, Xception).
View Article and Find Full Text PDFEntropy (Basel)
July 2025
University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, 1000 Ljubljana, Slovenia.
Machine learning techniques have revolutionized physical layer security (PLS) and provided opportunities for optimizing the performance and security of modern communication systems. In this study, we propose the first machine learning-assisted random communication system (ML-RCS). It comprises a pretrained decision tree (DT)-based receiver that extracts binary information from the transmitted random noise carrier signals.
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