Label-free DNA quantification by multi-frequency impedance cytometry and machine learning analysis

  • Jianye Sui
  • , Neeru Gandotra
  • , Pengfei Xie
  • , Zhongtian Lin
  • , Curt Scharfe
  • , Mehdi Javanmard

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

DNA quantification plays an important role in clinical diagnosis and research. A number of DNA quantification methods have been established, such as fluorescence imaging. In this work, we demonstrate a new label-free approach for DNA recognition by using multi-frequency impedance cytometry with machine learning.

Original languageEnglish (US)
Title of host publication21st International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2017
PublisherChemical and Biological Microsystems Society
Pages515-516
Number of pages2
ISBN (Electronic)9780692941836
StatePublished - 2020
Event21st International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2017 - Savannah, United States
Duration: Oct 22 2017Oct 26 2017

Publication series

Name21st International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2017

Conference

Conference21st International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2017
Country/TerritoryUnited States
CitySavannah
Period10/22/1710/26/17

All Science Journal Classification (ASJC) codes

  • Chemical Engineering (miscellaneous)
  • Bioengineering

Keywords

  • DNA quantification
  • Impedance cytometry
  • Label-free
  • Machine learning

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