A new genetic algorithm in proteomics: feature selection for SELDI-TOF data

DOI10.1016/j.csda.2008.02.025zbMath1452.62843OpenAlexW2074870400MaRDI QIDQ1023781

Christelle Reynès, Sylvain Lehmann, Nicolas Molinari, Robert Sabatier

Publication date: 16 June 2009

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

Full work available at URL: https://hal.inrae.fr/hal-02655748/file/Publis08-lasb-040_1.pdf

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) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (1)

Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification

Uses Software

• Matlab
• R
• WaveSpect0
• ElemStatLearn

Cites Work

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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Genetic algorithms and their statistical applications: an introduction
• Variable selection in regression models using nonstandard optimisation of information criteria
• Selection of artificial neural network models for survival analysis with genetic algorithms
• Wavelet-based procedures for proteomic mass spectrometry data processing
• Genetic algorithms: Principles and perpectives. A guide to GA theory
• Bayesian curve-fitting with free-knot splines
• Detection of cancer-specific markers amid massive mass spectral data
• A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection
• The elements of statistical learning. Data mining, inference, and prediction

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