Correction for 'A machine learning approach-based array sensor for rapidly predicting the mechanisms of action of antibacterial compounds' by Zhijun Li , , 2022, , 3087-3096, DOI: 10.1039/D1NR07452K.
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http://dx.doi.org/10.1039/d2nr90050e | DOI Listing |
Adv Sci (Weinh)
January 2025
Department of Laboratory Medicine, Guangdong Provincial Key Laboratory of Precision Medical Diagnostics, Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Guangdong Provincial Key Laboratory of Single Cell Technology and Application, School of Laboratory Medicine and Biotechnology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Circular RNAs in extracellular vesicles (EV-circRNAs) are gaining recognition as potential biomarkers for the diagnosis of gastric cancer (GC). Most current research is focused on identifying new biomarkers and their functional significance in disease regulation. However, the practical application of EV-circRNAs in the early diagnosis of GC is yet to be thoroughly explored due to the low accuracy of EV-circRNAs analysis.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, Bonn D-53115, Germany.
Explaining the predictions of machine learning models is of critical importance for integrating predictive modeling in drug discovery projects. We have generated a test system for predicting isoform selectivity of phosphoinositide 3-kinase (PI3K) inhibitors and systematically analyzed correct predictions of selective inhibitors using a new methodology termed MolAnchor, which is based on the "anchors" concept from explainable artificial intelligence. The approach is designed to generate chemically intuitive explanations of compound predictions.
View Article and Find Full Text PDFPsychiatry Clin Neurosci
January 2025
Department of Neuropsychiatry, Kochi Medical School, Kochi University, Kochi, Japan.
Aim: Despite the clinical importance and significant social burden of neuropsychiatric symptoms (NPS) in dementia, the underlying neurobiological mechanism remains poorly understood. Recently, neuroimaging-derived brain-age estimation by machine-learning analysis has shown promise as an individual-level biomarker. We investigated the relationship between NPS and brain-age in amnestic mild cognitive impairment (MCI) and early dementia.
View Article and Find Full Text PDFSci Rep
January 2025
Research Centre for Information and Communications Technologies (CITIC-UGR) - Department of Computer Engineering, Automation, and Robotics (ICAR), University of Granada, 18071, Granada, Spain.
NPJ Digit Med
January 2025
Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, United Kingdom.
Machine learning has increasingly been applied to predict opioid-related harms due to its ability to handle complex interactions and generating actionable predictions. This review evaluated the types and quality of ML methods in opioid safety research, identifying 44 studies using supervised ML through searches of Ovid MEDLINE, PubMed and SCOPUS databases. Commonly predicted outcomes included postoperative opioid use (n = 15, 34%) opioid overdose (n = 8, 18%), opioid use disorder (n = 8, 18%) and persistent opioid use (n = 5, 11%) with varying definitions.
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