Schachtner, R. and Lutter, D. and Knollmueller, P. and Tome, A. M. and Theis, F. J. and Schmitz, G. and Stetter, M. and Vilda, P. Gomez and Lang, E. W. (2008) Knowledge-based gene expression classification via matrix factorization. BIOINFORMATICS, 24 (15). pp. 1688-1697. ISSN 1367-4803,
Full text not available from this repository. (Request a copy)Abstract
Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | INDEPENDENT COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES; MICROARRAY DATA; RANDOM FOREST; SELECTION; CANCER; ARRAYS; TOOL; |
| Subjects: | 500 Science > 570 Life sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 26 Oct 2020 10:03 |
| Last Modified: | 26 Oct 2020 10:03 |
| URI: | https://pred.uni-regensburg.de/id/eprint/30496 |
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