BioViz: An Interactive Visualization Engine for the Design of Digital Microfluidic Biochips

Stoppe, Jannis; Keszöcze, Oliver; Luenert, Maximilian; Wille, Robert; Drechsler, Rolf

doi:10.1109/isvlsi.2017.38

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…This demonstrates a biochip architecture layout that was able to ease and speed up the design of a biochips. Stoppe et al [10] created a more interactive visualisation showing the droplet movement of analytes for a single-target biochip. This consists of two-dimensional electrical grids that are controlled by electrodes and their electrical actuation and includes the chip layout as well as various chip properties.…”

Section: Related Workmentioning

confidence: 99%

Information Visualisation for Antibiotic Detection Biochip Design and Testing

Craig

Tefsen

et al. 2022

Processes

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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.

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Section: Related Workmentioning

confidence: 99%

Information Visualisation for Antibiotic Detection Biochip Design and Testing

Craig

Tefsen

et al. 2022

Processes

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“…In practice, for plant phenomics data analysis, a combination of visual and statistical data analytics approach emerges [7]. The concept of visual knowledge discovery, which has been extensively utilized in a wide variety of data mining and machine learning applications [10], [15]- [17], [42], adopts the unique human perceptual and cognitive abilities and combines it with modern machine learning techniques, leading to accelerated multi-dimensional pattern recognition [21].…”

Section: Introductionmentioning

confidence: 99%

“…In the last decade, with the rapid development of data-driven techniques, many data visualization tools have been developed to visually recognize patterns and trends in big data and to facilitate statistical analysis and data mining [16], [35], [42]. Among all the data visualization techniques, a particularly useful tool is the heat map, which is a graphical representation of data making full use of color to extend perception into multiple dimensions by allowing properties to be mapped both spatially and as color gradients [49].…”

Section: Introductionmentioning

confidence: 99%

OLIVER: A Tool for Visual Data Analysis on Longitudinal Plant Phenomics Data

Tessmer

Kramer

Chen

2018

Preprint

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There is a critical unmet need for new tools to analyze and understand "big data" in the biological sciences where breakthroughs come from connecting massive genomics data with complex phenomics data. By integrating instant data visualization and statistical hypothesis testing, we have developed a new tool called OLIVER for phenomics visual data analysis with a unique function that any user adjustment will trigger realtime display updates for any affected elements in the workspace. By visualizing and analyzing omics data with OLIVER, biomedical researchers can quickly generate hypotheses and then test their thoughts within the same tool, leading to efficient knowledge discovery from complex, multi-dimensional biological data. The practice of OLIVER on multiple plant phenotyping experiments has shown that OLIVER can facilitate scientific discoveries. In the use case of OLIVER for large-scale plant phenotyping, a quick visualization identified emergent phenotypes that are highly transient and heterogeneous. The unique circular heat map with false-color plant images also indicates that such emergent phenotypes appear in different leaves under different conditions, suggesting that such previously unseen processes are critical for plant responses to dynamic environments.

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