Gene selection from microarray data for cancer classification -- a machine learning approach

DOI10.1016/j.compbiolchem.2004.11.001zbMath1095.92040OpenAlexW1972785704WikidataQ30981694 ScholiaQ30981694MaRDI QIDQ2500262

Igor V. Tetko, Eibe Frank, Axel Facius, Yu Wang, Mark A. Hall, Klaus F. X. Mayer, Hans-Werner Mewes

Publication date: 23 August 2006

Published in: Computational Biology and Chemistry (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.compbiolchem.2004.11.001

zbMATH Keywords

machine learningfeature selectionmicroarraygene selectioncancer classification

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Medical applications (general) (92C50) Biochemistry, molecular biology (92C40)

Related Items (7)

Forward selection method with regression analysis for optimal gene selection in cancer classification ⋮ Penalized model-based clustering ⋮ Penalized model-based clustering with unconstrained covariance matrices ⋮ Cancer feature selection and classification using a binary quantum-behaved particle swarm optimization and support vector machine ⋮ Applications of Bayesian gene selection and classification with mixtures of generalized singular \(g\)-priors ⋮ Exploiting scale-free information from expression data for cancer classification ⋮ Hybrid Classification of High-Dimensional Biomedical Tumour Datasets

Uses Software

• RankGene

Cites Work

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