Stable feature selection for biomarker discovery

DOI10.1016/j.compbiolchem.2010.07.002zbMath1403.92068OpenAlexW2162162988WikidataQ37779401 ScholiaQ37779401MaRDI QIDQ1631176

Zengyou He, Weichuan Yu

Publication date: 5 December 2018

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

Full work available at URL: https://doi.org/10.1016/j.compbiolchem.2010.07.002

zbMATH Keywords

stabilitymachine learningfeature selectionbiomarker discovery

Mathematics Subject Classification ID

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

Related Items (11)

Efficient projection algorithms onto the weighted \(\ell_1\) ball ⋮ Incorporation of gene exchangeabilities improves the reproducibility of gene set rankings ⋮ Network-based feature screening with applications to genome data ⋮ Extensions of stability selection using subsamples of observations and covariates ⋮ Grouped variable importance with random forests and application to multiple functional data analysis ⋮ A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data ⋮ Grouped feature importance and combined features effect plot ⋮ More power via graph-structured tests for differential expression of gene networks ⋮ SIRUS: stable and interpretable RUle set for classification ⋮ Pathway-level disease data mining through hyper-box principles ⋮ Stable attribute reduction for neighborhood rough set

Uses Software

• AdaBoost.MH
• GSA
• Bolasso

Cites Work

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• Bagging predictors
• Identification of cancer-associated gene clusters and genes via clustering penalization
• On testing the significance of sets of genes
• A decision-theoretic generalization of on-line learning and an application to boosting
• Comparing the Characteristics of Gene Expression Profiles Derived by Univariate and Multivariate Classification Methods
• Averaged gene expressions for regression
• Regularization and Variable Selection Via the Elastic Net
• Gene selection for cancer classification using support vector machines

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