Machine learning for the meta-analyses of microbial pathogens’ volatile signatures
| Autor: | Ana P. Traguedo, Maria J. Frias, Ana R. Porteira, Ana C. A. Roque, Susana I. C. J. Palma, Hugo Gamboa |
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| Přispěvatelé: | UCIBIO - Applied Molecular Biosciences Unit, DQ - Departamento de Química, DF – Departamento de Física, LIBPhys-UNL |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Computer science lcsh:Medicine Human pathogen Diagnostic tools Machine learning computer.software_genre 01 natural sciences Article 03 medical and health sciences SDG 3 - Good Health and Well-being Breath acetone General lcsh:Science Pathogen Volatile Organic Compounds Multidisciplinary business.industry 010401 analytical chemistry lcsh:R 3. Good health 0104 chemical sciences 030104 developmental biology Breath Tests lcsh:Q Artificial intelligence business computer |
| Zdroj: | Scientific Reports Scientific Reports, Vol 8, Iss 1, Pp 1-15 (2018) Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| ISSN: | 2045-2322 |
| Popis: | info:eu-repo/grantAgreement/FCT/5876/147258/PT This work was supported by the European Research Council through the grant reference SCENT-ERC-2014-STG-639123 (2015-2020), and by the Unidade de Ciencias Biomoleculares Aplicadas-UCIBIO, which is financed by national funds from FCT/MEC (UID/Multi/04378/2013) and co-financed by the ERDF under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007728). Non-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing. Artificial intelligence is increasingly recognised as an essential tool in health sciences. Machine learning algorithms based in support vector machines and features selection tools were here applied to find sets of microbial VOCs with pathogen-discrimination power. Studies reporting VOCs emitted by human microbial pathogens published between 1977 and 2016 were used as source data. A set of 18 VOCs is sufficient to predict the identity of 11 microbial pathogens with high accuracy (77%), and precision (62-100%). There is one set of VOCs associated with each of the 11 pathogens which can predict the presence of that pathogen in a sample with high accuracy and precision (86-90%). The implemented pathogen classification methodology supports future database updates to include new pathogen-VOC data, which will enrich the classifiers. The sets of VOCs identified potentiate the improvement of the selectivity of non-invasive infection diagnostics using artificial olfaction devices. publishersversion published |
| Databáze: | OpenAIRE |
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