Nonlinear Sparse Component Analysis with a Reference: Variable Selection in Genomics and Proteomics (CROSBI ID 626549)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Kopriva, Ivica ; Kapitanović, Sanja ; Čačev, Tamara
engleski
Nonlinear Sparse Component Analysis with a Reference: Variable Selection in Genomics and Proteomics
Many scenarios occurring in genomics and proteomics involve small number of labeled data and large number of variables. To create prediction models robust to overfitting variable selection is necessary. We propose variable selection method using nonlinear sparse component analysis with a reference representing either negative (healthy) or positive (cancer) class. Thereby, component comprised of cancer related variables is automatically inferred from the geometry of nonlinear mixture model with a reference. Proposed method is compared with 3 supervised and 2 unsupervised variable selection methods on two-class problems using 2 genomic and 2 proteomic datasets. Obtained results, which include analysis of biological relevance of selected genes, are comparable with those achieved by supervised methods. Thus, proposed method can possibly perform better on unseen data of the same cancer type.
Variable selection; Nonlinear mixture model; Empirical kernel maps; Sparse component analysis
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Podaci o prilogu
168-175.
2015.
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objavljeno
978-3-319-22482-4
Podaci o matičnoj publikaciji
12th International Conference, LVA/ICA 2015, Liberec, Czech Republic, August 25-28, 2015, Proceedings
Vincent, Emanuel ; Yeredor, Ari ; Kolodovsky, Zbinyek ; Tichavsky, Petr
Heidelberg: Springer
Podaci o skupu
12th International Conference on Latent Variable Analysis and Signal Separation LVA/ICA 2015
pozvano predavanje
25.08.2015-28.08.2015
Liberec, Češka Republika