ArticlePloS one2016
An NMF-L2,1-Norm Constraint Method for Characteristic Gene Selection.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.PLoS computational biology · 2023Article
- Gene Correlation Guided Gene Selection for Microarray Data Classification.BioMed research international · 2021Article
- Gene selection for microarray data classification via subspace learning and manifold regularization.Medical & biological engineering & computing · 2018Article
- Identifying drug-pathway association pairs based on LBMC systems biology · 2017Article
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Authors and funding
6 authors.
Funding
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Abstract
Recent research has demonstrated that characteristic gene selection based on gene expression data remains faced with considerable challenges. This is primarily because gene expression data are typically high dimensional, negative, non-sparse and noisy. However, existing methods for data analysis are able to cope with only some of these challenges. In this paper, we address all of these challenges with a unified method: nonnegative matrix factorization via the L2,1-norm (NMF-L2,1). While L2,1-norm minimization is applied to both the error function and the regularization term, our method is robust to outliers and noise in the data and generates sparse results. The application of our method to plant and tumor gene expression data demonstrates that NMF-L2,1 can extract more characteristic genes than other existing state-of-the-art methods.
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