TY - GEN
T1 - A P-value Glyph Matrix Visualisation for Multi-Target Biochip Biosensor Selection
AU - Craig, Paul
AU - Ng, Ruben
AU - Tefsen, Boris
AU - Linsen, Sam
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper introduces a novel glyph-based visualisation to help biologists evaluate the effectiveness of different biosensors to be used with multi-target biochips. The interface divides into two representation, first is the plate view, where visualising the replication of biosensors plate experiment, and the second is the matrix view which implements glyph-based techniques, a notable graphic or representation, which helps the biologist to determine which biosensors have the best ability to detect and identify analytes by obtaining RGB values and showing the results of t-tests comparing results for biosensors in either a control environment or exposed to different analytes. This is done for different exposure times and for different biosensors to show which biosensors are most effective at detecting and differentiating between analytes over different periods. The biosensors experiment is done for fifteen biosensors within thirty seconds with two types of antibiotics and a control (no antibiotic). Several patterns of biosensors were found after using this interface. Therefore, the resulting visualisation helps biologists to select the most effective biosensors for the design of a multi-target bio-chip to detect a variety of analytes for a given range of exposure times.
AB - This paper introduces a novel glyph-based visualisation to help biologists evaluate the effectiveness of different biosensors to be used with multi-target biochips. The interface divides into two representation, first is the plate view, where visualising the replication of biosensors plate experiment, and the second is the matrix view which implements glyph-based techniques, a notable graphic or representation, which helps the biologist to determine which biosensors have the best ability to detect and identify analytes by obtaining RGB values and showing the results of t-tests comparing results for biosensors in either a control environment or exposed to different analytes. This is done for different exposure times and for different biosensors to show which biosensors are most effective at detecting and differentiating between analytes over different periods. The biosensors experiment is done for fifteen biosensors within thirty seconds with two types of antibiotics and a control (no antibiotic). Several patterns of biosensors were found after using this interface. Therefore, the resulting visualisation helps biologists to select the most effective biosensors for the design of a multi-target bio-chip to detect a variety of analytes for a given range of exposure times.
KW - Information Visualization
KW - bio-informatics
UR - http://www.scopus.com/inward/record.url?scp=85186765255&partnerID=8YFLogxK
U2 - 10.1109/CSECS60003.2023.10428255
DO - 10.1109/CSECS60003.2023.10428255
M3 - Conference Proceeding
AN - SCOPUS:85186765255
T3 - 2023 6th International Conference on Software Engineering and Computer Science, CSECS 2023
BT - 2023 6th International Conference on Software Engineering and Computer Science, CSECS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Software Engineering and Computer Science, CSECS 2023
Y2 - 22 December 2023 through 24 December 2023
ER -