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CutPointVis: An Interactive Exploration Tool for Cancer Biomarker Cutpoint Optimization

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

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Abstract

In the field of medical and epidemiological research, it is a common practice to do a clinical or statistical dichotomization of a continuous variable. By dichotomizing a continuous variable, a researcher can build a eligibility criteria for potential studies, predict disease likelihood or predict treatment response. The dichotomization methods can be classified into data-depend methods and outcome-based methods. The data-dependent methods are considered to be arbitrary and lack of generics. While the outcome-based methods compute an optimal cut point which maximizes the statistical difference between two dichotomized groups. There is no standard software yet for an expedited cut point determination In this work, we present CutPointVis, a visualization platform for fast and convenient optimal cut point determination. Compared to existing research work, CutPointVis distinguishes itself with its realtime feature and better user interactivity. A case study is presented to demonstrate the usability of CutPointVis.

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Notes

  1. 1.

    A demonstrative example of CancerVis (CutPointVis) can be found at http://grid.cs.gsu.edu/~lzhang14/demo/biocancer1/main.html. Please be noted that since the system is still under development, the demonstrative example uses a given dataset for exploration use.

  2. 2.

    The CutPointVis tool is still under development. Users are not allowed to change dataset in the cloud yet. The current dataset for demo is extracted from gene expression series GSE2034.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Georgia State University, Atlanta, Georgia, 30303, USA

    Lei Zhang & Ying Zhu

Authors
  1. Lei Zhang
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  2. Ying Zhu
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Corresponding author

Correspondence to Lei Zhang .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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Zhang, L., Zhu, Y. (2016). CutPointVis: An Interactive Exploration Tool for Cancer Biomarker Cutpoint Optimization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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