Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) emitted from biological media has seen increas...

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Veröffentlicht in:	Metabolites Jg. 12; H. 3; S. 232
Hauptverfasser:	Arora, Mehak, Zambrzycki, Stephen C., Levy, Joshua M., Esper, Annette, Frediani, Jennifer K., Quave, Cassandra L., Fernández, Facundo M., Kamaleswaran, Rishikesan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 08.03.2022 MDPI
Schlagworte:	Algorithms ambient plasma ionization Antibiotic resistance Artificial intelligence Bacteria Biomarkers Chromatography Classification Clustering DART-MS Datasets Decision trees Fungi Gases Gene expression imbalanced learning K-means clustering Learning algorithms Machine learning machine learning classification algorithms Mass spectrometry Mass spectroscopy Organic compounds Pathogens Physiology Scientific imaging Skin Solid phase methods solid phase micro-extraction Support vector machines VOCs Volatile organic compounds imbalanced learning pathogen identification ambient plasma ionization DART-MS machine learning classification algorithms solid phase micro-extraction VOC point-of-care devices K-means clustering
ISSN:	2218-1989, 2218-1989
Online-Zugang:	Volltext
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