VISDA: an open-source caBIG™ analytical tool for data clustering and beyond | Zendy

Jiajing Wang | Zendy; Huai Li | Zendy; Yitan Zhu | Zendy; Malik Yousef | Zendy; Michael Nebozhyn | Zendy; Michael K. Showe | Zendy; Louise C. Showe | Zendy; Jianhua Xuan | Zendy; Robert Clarke | Zendy; Yue Wang | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

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

Author(s) -

Jiajing Wang,

Huai Li,

Yitan Zhu,

Malik Yousef,

Michael Nebozhyn,

Michael K. Showe,

Louise C. Showe,

Jianhua Xuan,

Robert Clarke,

Yue Wang

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm290

Subject(s) - computer science , cluster analysis , visualization , hierarchical clustering , exploit , data science , data exploration , open source , data mining , information retrieval , machine learning , computer security , software , programming language

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research