PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer | Zendy

Andrew E. Teschendorff | Zendy; Ali Naderi | Zendy; Nuno L. BarbosaMorais | Zendy; Carlos Caldas | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer

Author(s) -

Andrew E. Teschendorff,

Ali Naderi,

Nuno L. BarbosaMorais,

Carlos Caldas

Publication year - 2006

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/btl174

Subject(s) - feature selection , false discovery rate , computer science , pattern recognition (psychology) , kurtosis , outlier , false positive paradox , artificial intelligence , cluster analysis , breast cancer , data mining , computational biology , cancer , mathematics , biology , statistics , gene , biochemistry , genetics

Elucidating the molecular taxonomy of cancers and finding biological and clinical markers from microarray experiments is problematic due to the large number of variables being measured. Feature selection methods that can identify relevant classifiers or that can remove likely false positives prior to supervised analysis are therefore desirable.

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