Intelligent fluorescence image analysis of giant unilamellar vesicles using convolutional neural network.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Fluorescence image analysis in biochemical science often involves the complex tasks of identifying samples for analysis and calculating the desired information from the intensity traces. Analyzing giant unilamellar vesicles (GUVs) is one of these tasks. Researchers need to identify many vesicles to statistically analyze the degree of molecular interaction or state of molecular organization on the membranes. This analysis is complicated, requiring a careful manual examination by researchers, so automating the analysis can significantly aid in improving its efficiency and reliability.

Authors

  • Il-Hyung Lee
    Department of Chemistry and Biochemistry, Montclair State University, Montclair, NJ, 07043, USA. leei@montclair.edu.
  • Sam Passaro
    Department of Chemistry and Biochemistry, Montclair State University, Montclair, NJ, 07043, USA.
  • Selin Ozturk
    Department of Chemistry and Biochemistry, Montclair State University, Montclair, NJ, 07043, USA.
  • Juan Ureña
    Department of Chemistry and Biochemistry, Montclair State University, Montclair, NJ, 07043, USA.
  • Weitian Wang
    Department of Computer Science, Montclair State University, Montclair, NJ, 07043, USA.