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Information Visualisation for Antibiotic Detection Biochip Design and Testing

  • Paul Craig*
  • , Ruben Ng
  • , Boris Tefsen
  • , Sam Linsen
  • , Yu Liu
  • , Joshua Hendel
  • *Corresponding author for this work
    • Xi'an Jiaotong-Liverpool University
    • Department of Molecular Microbiology
    • Utrecht University
    • Ronin Institute
    • Ltd

    Research output: Contribution to journal › Article › peer-review

    1 Citation (Scopus)

    Abstract

    Biochips are engineered substrates that have different spots that change colour according to biochemical reactions. These spots can be read together to detect different analytes (such as different types of antibiotic, pathogens, or biological agents). While some chips are designed so that each spot on its own can detect a particular analyte, chip designs that use a combination of spots to detect different analytes can be more efficient and detect a larger number of analytes with a smaller number of spots. These types of chip can, however, be more difficult to design, as an efficient and effective combination of biosensors needs to be selected for the chip. These need to be able to differentiate between a range of different analytes so the values can be combined in a way that demonstrates the confidence that a particular analyte is present or not. The study described in this paper examines the potential for information visualisation to support the process of designing and reading biochips by developing and evaluating applications that allow biologists to analyse the results of experiments aimed at detecting candidate bio-sensors (to be used as biochip spots) and examining how biosensors can combine to identify different analytes. Our results demonstrate the potential of information visualisation and machine learning techniques to improve the design of biochips.

    Original languageEnglish
    Article number2680
    JournalProcesses
    Volume10
    Issue number12
    DOIs
    Publication statusPublished - Dec 2022

    Keywords

    • bioinformatics
    • information visualisation
    • machine learning

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