Clinically driven semi-supervised class discovery in gene expression data | Zendy

Israel Steinfeld | Zendy; Roy Navon | Zendy; Diego Ardigò | Zendy; Ivana Zavaroni | Zendy; Zohar Yakhini | Zendy

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Clinically driven semi-supervised class discovery in gene expression data

Author(s) -

Israel Steinfeld,

Roy Navon,

Diego Ardigò,

Ivana Zavaroni,

Zohar Yakhini

Publication year - 2008

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btn279

Subject(s) - class (philosophy) , computational biology , computer science , gene expression , artificial intelligence , expression (computer science) , gene , data mining , biology , genetics , programming language

Unsupervised class discovery in gene expression data relies on the statistical signals in the data to exclusively drive the results. It is often the case, however, that one is interested in constraining the search space to respect certain biological prior knowledge while still allowing a flexible search within these boundaries.

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