Predicting relapse prior to transplantation in chronic myeloid leukemia by integrating expert knowledge and expression data | Zendy

Ka Yee Yeung | Zendy; Ted Gooley | Zendy; A. Zhang | Zendy; Adrian E. Raftery | Zendy; Jerald P. Radich | Zendy; Vivian G. Oehler | Zendy

Open Access

Predicting relapse prior to transplantation in chronic myeloid leukemia by integrating expert knowledge and expression data

Author(s) -

Ka Yee Yeung,

Ted Gooley,

A. Zhang,

Adrian E. Raftery,

Jerald P. Radich,

Vivian G. Oehler

Publication year - 2012

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/bts059

Subject(s) - myeloid leukemia , transplantation , gene signature , gene expression , bayesian probability , gene , computational biology , oncology , bioinformatics , data mining , medicine , computer science , biology , artificial intelligence , immunology , genetics

Selecting a small number of signature genes for accurate classification of samples is essential for the development of diagnostic tests. However, many genes are highly correlated in gene expression data, and hence, many possible sets of genes are potential classifiers. Because treatment outcomes are poor in advanced chronic myeloid leukemia (CML), we hypothesized that expression of classifiers of advanced phase CML when detected in early CML [chronic phase (CP) CML], correlates with subsequent poorer therapeutic outcome.

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