Boosting for tumor classification with gene expression data | Zendy

Marcel Dettling | Zendy; Peter Bühlmann | Zendy

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Boosting for tumor classification with gene expression data

Author(s) -

Marcel Dettling,

Peter Bühlmann

Publication year - 2003

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/btf867

Subject(s) - boosting (machine learning) , categorical variable , computer science , classifier (uml) , machine learning , gradient boosting , artificial intelligence , decision tree , data mining , pattern recognition (psychology) , random forest

Microarray experiments generate large datasets with expression values for thousands of genes but not more than a few dozens of samples. Accurate supervised classification of tissue samples in such high-dimensional problems is difficult but often crucial for successful diagnosis and treatment. A promising way to meet this challenge is by using boosting in conjunction with decision trees.

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