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Author: Ivica Kopriva

Affiliation: Rudjer Boskovich Institute, Croatia

Keyword(s): Variable Selection, Nonlinear Mixture Models, Explicit Feature Maps, Sparse Component Analysis.

Related Ontology Subjects/Areas/Topics: Algorithms and Software Tools ; Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Computational Intelligence ; Genomics and Proteomics ; Pattern Recognition, Clustering and Classification ; Soft Computing

Abstract: 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.

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Paper citation in several formats:
Kopriva, I. (2015). A Nonlinear Mixture Model based Unsupervised Variable Selection in Genomics and Proteomics. In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2015) - BIOINFORMATICS; ISBN 978-989-758-070-3; ISSN 2184-4305, SciTePress, pages 85-92. DOI: 10.5220/0005161700850092

@conference{bioinformatics15,
author={Ivica Kopriva.},
title={A Nonlinear Mixture Model based Unsupervised Variable Selection in Genomics and Proteomics},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2015) - BIOINFORMATICS},
year={2015},
pages={85-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005161700850092},
isbn={978-989-758-070-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2015) - BIOINFORMATICS
TI - A Nonlinear Mixture Model based Unsupervised Variable Selection in Genomics and Proteomics
SN - 978-989-758-070-3
IS - 2184-4305
AU - Kopriva, I.
PY - 2015
SP - 85
EP - 92
DO - 10.5220/0005161700850092
PB - SciTePress

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