A method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer | Zendy

Wensheng Zhang | Zendy; Romdhane Rekaya | Zendy; Keith Bertrand | Zendy

Open Access

A method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer

Author(s) -

Wensheng Zhang,

Romdhane Rekaya,

Keith Bertrand

Publication year - 2005

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/bti738

Subject(s) - computer science , machine learning , breast cancer , matlab , disease , sample (material) , artificial intelligence , training set , sample size determination , oestrogen receptor , data mining , pattern recognition (psychology) , cancer , statistics , medicine , pathology , mathematics , chemistry , chromatography , operating system

An accurate diagnostic and prediction will not be achieved unless the disease subtype status for every training sample used in the supervised learning step is accurately known. Such an assumption requires the existence of a perfect tool for disease diagnostic and classification, which is seldom available in the majority of the cases. Thus, the supervised learning step has to be conducted with a statistical model that contemplates and handles potential mislabeling in the input data.

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