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The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease.
Proteomics
January 2025
School of Public Health, University of Haifa, Haifa, Israel.
Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection and slowing disease progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate and proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on the molecular mechanisms of CKD, helping to identify biomarkers for disease assessment and management.
View Article and Find Full Text PDFA High-Throughput Screening Pipeline to Identify Methyltransferase and Exonuclease Inhibitors of SARS-CoV-2 NSP14.
Biochemistry
January 2025
National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850, United States.
SARS-CoV-2 infections led to a worldwide pandemic in 2020. As of 2024, therapeutics against SARS-CoV-2 have continued to be desirable. NSP14 is a dual-function methyltransferase (MTase) and exonuclease (ExoN) with key roles in SARS-CoV-2 genome propagation and host immune system evasion.
View Article and Find Full Text PDFG-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation.
Int J Comput Assist Radiol Surg
January 2025
Computer Vision and Image Processing Lab., UofL, Louisville, KY, 40292, USA.
Purpose: This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settings.
Methods: The proposed approach integrates 3D contextual information via guided sequential episodic training in which a query CT slice is segmented by exploiting its previous labeled CT slice (i.e.
Modular comparison of untargeted metabolomics processing steps.
Anal Chim Acta
January 2025
Christian Doppler Laboratory for Innovative Gut Health Concepts of Livestock, Austria; BOKU University, Vienna, Dept. IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, Tulln, Austria.
Background: Untargeted metabolomics requires robust and reliable strategies for data processing to extract relevant information form the underlying raw data. Multiple platforms for data processing are available, but the choice of software tool can have an impact on the analysis. This study provides a comprehensive evaluation of four workflows based on commonly used metabolomics software tools: XCMS, Compound Discoverer, MS-DIAL, and MZmine.
View Article and Find Full Text PDFLeveraging Transformers-based models and linked data for deep phenotyping in radiology.
Comput Methods Programs Biomed
January 2025
Laberit, Avda. de Catalunya, 9, València, 46020, Spain.
Background And Objective: Despite significant investments in the normalization and the standardization of Electronic Health Records (EHRs), free text is still the rule rather than the exception in clinical notes. The use of free text has implications in data reuse methods used for supporting clinical research since the query mechanisms used in cohort definition and patient matching are mainly based on structured data and clinical terminologies. This study aims to develop a method for the secondary use of clinical text by: (a) using Natural Language Processing (NLP) for tagging clinical notes with biomedical terminology; and (b) designing an ontology that maps and classifies all the identified tags to various terminologies and allows for running phenotyping queries.
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