VISDA: An open-source caBIG™ analytical tool for data clustering and beyond

Jiajing Wang, Huai Li, Yitan Zhu, Malik Yousef, Michael Nebozhyn, Michael Showe, Louise Showe, Jianhua Xuan, Robert Clarke, Yue Wang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Summary: VISDA (Visual Statistical Data Analyzer) is a caBIG™ analytical tool for cluster modeling, visualization and discovery that has met silver-level compatibility under the caBIG initiative. Being statistically principled and visually interfaced, VISDA exploits both hierarchical statistics modeling and human gift for pattern recognition to allow a progressive yet interactive discovery of hidden clusters within high dimensional and complex biomedical datasets. The distinctive features of VISDA are particularly useful for users across the cancer research and broader research communities to analyze complex biological data.

Original languageEnglish (US)
Pages (from-to)2024-2027
Number of pages4
JournalBioinformatics
Volume23
Issue number15
DOIs
StatePublished - Aug 1 2007
Externally publishedYes

Bibliographical note

Funding Information:
The authors would like to thank members of the caBIGTM Integrative Cancer Research WorkSpace for reviewing VISDA documentation and/or providing helpful feedback on VISDA development. Thanks also go to the caArray Team at the National Cancer Institute for providing caArray testing datasets. This work is supported by the National Cancer Institute of the NIH under Grants caBIGTM-VISDA; CA109872; CA100970; CA096483.

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