Feature selection and machine learning with mass spectrometry data for distinguishing cancer and non-cancer samples

DOI10.1016/j.stamet.2005.09.006zbMath1248.62212OpenAlexW2009876524MaRDI QIDQ713690

Susmita Datta, Lara M. Depadilla

Publication date: 19 October 2012

Published in: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.stamet.2005.09.006

zbMATH Keywords

classificationclusteringmicroarrayshigh throughput data

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Medical applications (general) (92C50)

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Uses Software

• R
• MASS (R)
• AS 136
• clusfind

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• Statistical Analysis of Financial Data in S-Plus
• Finding Groups in Data
• Detection of cancer-specific markers amid massive mass spectral data
• Self-organizing maps.
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests

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