New normalization methods using support vector machine quantile regression approach in microarray analysis

DOI10.1016/j.csda.2008.02.006zbMath1452.62848OpenAlexW2062210993MaRDI QIDQ1023756

Insuk Sohn, Sujong Kim, Changha Hwang, Jae Won Lee

Publication date: 16 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2008.02.006

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Protein sequences, DNA sequences (92D20)

Related Items (max. 100)

Fast quantile regression in reproducing kernel Hilbert space ⋮ Learning Multiple Quantiles With Neural Networks ⋮ Editorial: Computational statistics within clinical research ⋮ Editorial: Statistical genetics \& statistical genomics: where biology, epistemology, statistics, and computation collide ⋮ A generalized Newton algorithm for quantile regression models

Uses Software

• LIBSVM
• sma

Cites Work

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• On a kernel-based method for pattern recognition, regression, approximation, and operator inversion
• An optimal choice of window width for LOWESS normalization of microarray data
• Significance analysis of microarrays applied to the ionizing radiation response
• Comparison of various statistical methods for identifying differential gene expression in replicated microarray data
• Robust Locally Weighted Regression and Smoothing Scatterplots
• Regression Quantiles

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