Nonlinear Sparse Component Analysis with a Reference: Variable Selection in Genomics and Proteomics

被引:0
作者
Kopriva, Ivica [1 ]
Kapitanovic, Sanja [2 ]
Cacev, Tamara [2 ]
机构
[1] Rudjer Boskovic Inst, Div Laser & Atom R&D, Zagreb 10000, Croatia
[2] Rudjer Boskovic Inst, Div Mol Med, Zagreb 10000, Croatia
来源
LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION, LVA/ICA 2015 | 2015年 / 9237卷
关键词
Variable selection; Nonlinear mixture model; Empirical kernel maps; Sparse component analysis; CANCER; CLASSIFICATION; ALGORITHMS; PATTERNS; DISCOVERY; SERUM;
D O I
10.1007/978-3-319-22482-4_19
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:168 / 175
页数:8
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