Machine learning classification of bacterial species using mix-and-match reagents on paper microfluidic chips and smartphone-based capillary flow analysis

Kim, Sangsik; Day, Alexander S.; Yoon, Jeong‐Yeol

doi:10.1007/s00216-022-04031-5

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Machine learning classification of bacterial species using mix-and-match reagents on paper microfluidic chips and smartphone-based capillary flow analysis

Cited by 6 publications

References 17 publications

Use of machine learning/artificial intelligence in chemical sensors and biosensors

Use of machine learning/artificial intelligence in chemical sensors and biosensors

Machine learning–assisted biosensors utilizing a set of biological polymers

Machine learning–assisted flow velocity analysis in paper microfluidics

Contact Info

Product

Resources

About