FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3198214009> ?p ?o ?g. }

• W3198214009 abstract "Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings." @default.
• W3198214009 created "2021-09-13" @default.
• W3198214009 creator A5009020573 @default.
• W3198214009 creator A5015011842 @default.
• W3198214009 creator A5020435715 @default.
• W3198214009 creator A5021687717 @default.
• W3198214009 creator A5022144249 @default.
• W3198214009 creator A5045025267 @default.
• W3198214009 creator A5057899498 @default.
• W3198214009 creator A5060177975 @default.
• W3198214009 creator A5086664647 @default.
• W3198214009 creator A5091667861 @default.
• W3198214009 date "2021-08-31" @default.
• W3198214009 modified "2023-10-14" @default.
• W3198214009 title "Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species" @default.
• W3198214009 cites W1685350083 @default.
• W3198214009 cites W1927124817 @default.
• W3198214009 cites W1980090425 @default.
• W3198214009 cites W1994607919 @default.
• W3198214009 cites W1996707645 @default.
• W3198214009 cites W2007473457 @default.
• W3198214009 cites W2012101065 @default.
• W3198214009 cites W2026117722 @default.
• W3198214009 cites W2033403400 @default.
• W3198214009 cites W2095129805 @default.
• W3198214009 cites W2113883062 @default.
• W3198214009 cites W2163085701 @default.
• W3198214009 cites W2167047714 @default.
• W3198214009 cites W2169840816 @default.
• W3198214009 cites W2266235606 @default.
• W3198214009 cites W2501077697 @default.
• W3198214009 cites W2559306277 @default.
• W3198214009 cites W2559479590 @default.
• W3198214009 cites W2618036898 @default.
• W3198214009 cites W2728834661 @default.
• W3198214009 cites W2765397480 @default.
• W3198214009 cites W2768771467 @default.
• W3198214009 cites W2776970688 @default.
• W3198214009 cites W2787601138 @default.
• W3198214009 cites W2825859508 @default.
• W3198214009 cites W2895894548 @default.
• W3198214009 cites W2904379906 @default.
• W3198214009 cites W2904525844 @default.
• W3198214009 cites W2910805901 @default.
• W3198214009 cites W2924468483 @default.
• W3198214009 cites W2945103729 @default.
• W3198214009 cites W2959236235 @default.
• W3198214009 cites W2982482221 @default.
• W3198214009 cites W2994164010 @default.
• W3198214009 cites W2996365453 @default.
• W3198214009 cites W2999277653 @default.
• W3198214009 cites W3004571359 @default.
• W3198214009 cites W3016187692 @default.
• W3198214009 cites W3016914938 @default.
• W3198214009 cites W3032972414 @default.
• W3198214009 cites W3048657004 @default.
• W3198214009 cites W3081623076 @default.
• W3198214009 cites W3091861253 @default.
• W3198214009 cites W3092890642 @default.
• W3198214009 cites W3138323391 @default.
• W3198214009 cites W3187529499 @default.
• W3198214009 cites W34534769 @default.
• W3198214009 cites W4251817184 @default.
• W3198214009 doi "https://doi.org/10.3389/fmicb.2021.696921" @default.
• W3198214009 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8439569" @default.
• W3198214009 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34531835" @default.
• W3198214009 hasPublicationYear "2021" @default.
• W3198214009 type Work @default.
• W3198214009 sameAs 3198214009 @default.
• W3198214009 citedByCount "31" @default.
• W3198214009 countsByYear W31982140092022 @default.
• W3198214009 countsByYear W31982140092023 @default.
• W3198214009 crossrefType "journal-article" @default.
• W3198214009 hasAuthorship W3198214009A5009020573 @default.
• W3198214009 hasAuthorship W3198214009A5015011842 @default.
• W3198214009 hasAuthorship W3198214009A5020435715 @default.
• W3198214009 hasAuthorship W3198214009A5021687717 @default.
• W3198214009 hasAuthorship W3198214009A5022144249 @default.
• W3198214009 hasAuthorship W3198214009A5045025267 @default.
• W3198214009 hasAuthorship W3198214009A5057899498 @default.
• W3198214009 hasAuthorship W3198214009A5060177975 @default.
• W3198214009 hasAuthorship W3198214009A5086664647 @default.
• W3198214009 hasAuthorship W3198214009A5091667861 @default.
• W3198214009 hasBestOaLocation W31982140091 @default.
• W3198214009 hasConcept C104047586 @default.
• W3198214009 hasConcept C11413529 @default.
• W3198214009 hasConcept C119857082 @default.
• W3198214009 hasConcept C120665830 @default.
• W3198214009 hasConcept C121332964 @default.
• W3198214009 hasConcept C153180895 @default.
• W3198214009 hasConcept C154945302 @default.
• W3198214009 hasConcept C169573571 @default.
• W3198214009 hasConcept C186060115 @default.
• W3198214009 hasConcept C192562407 @default.
• W3198214009 hasConcept C2777790068 @default.
• W3198214009 hasConcept C40003534 @default.
• W3198214009 hasConcept C41008148 @default.
• W3198214009 hasConcept C46576248 @default.
• W3198214009 hasConcept C73555534 @default.
• W3198214009 hasConcept C8038995 @default.

• next