Naslov
A Nonlinear Mixture Model Based Unsupervised Variable Selection in Genomics and Proteomics

Autori
Kopriva, Ivica

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Bioinformatics 2015 6th International Conference on Bioinformatics Models, Methods and Algorithms / Gamboa, Hugo ; Fred, Ana ; Elias Dirk ; Pastor, Oscar ; Sinoquet, Christine - Lisabon : SCITEPRESS, 2015, 85-92

ISBN
978-989-758-070-3

Skup
Bioinformatics 2015 6th International Conference on Bioinformatics Models, Methods and Algorithms

Mjesto i datum
Lisabon, Portugal, 12.01.2015. - 15.01.2015

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
variable selection; nonlinear mixture models; explicit feature maps; sparse component analysis

Sažetak
Typical scenarios occurring in genomics and proteomics involve small number of samples and large number of variables. Thus, variable selection is necessary for creating disease prediction models robust to overfitting. We propose an unsupervised variable selection method based on sparseness constrained decomposition of a sample. Decomposition is based on nonlinear mixture model comprised of test sample and a reference sample representing negative (healthy) class. Geometry of the model enables automatic selection of component comprised of disease related variables. Proposed unsupervised variable selection method is compared with 3 supervised and 1 unsupervised variable selection methods on two-class problems using 3 genomic and 2 proteomic data sets. Obtained results suggest that proposed method could perform better than supervised methods on unseen data of the same cancer type.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Biologija, Računarstvo

POVEZANOST RADA