Abstract
When cancer breaks out, central processes in the cell are disturbed. These disturbances are often due to abnormalities in gene expression. The microarray technology allows to monitor the expression of thousands of genes in human cells simultaneously. It is common knowledge that tumor cells show different gene expression profiles compared to normal tissue but also to tissue obtained from metastases. However, the identification of biomarkers, that is sets of genes whose expression change is highly correlated with the disease, poses a great challenge. Increasingly important is the extraction of combinatorial biomarkers. Here, the correlation to the disease is a result of the joint expression of several genes, whereas the single genes do not necessarily distinguish well between healthy and diseased tissue types. In this paper we describe how formal concept analysis can be used to identify gene combinations that are able to distinguish between tumor- and metastasis tissue in breast cancer based on microarray gene expression data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alexe, G., et al.: Breast cancer prognosis by combinatorial analysis of gene expression data. Breast Cancer Research 8(4) (2006)
Alexe, G., et al.: Logical analysis of diffuse large b-cell lymphomas. Artificial Intelligence in Medicine 34(3), 235–267 (2005)
Alexe, G., et al.: Ovarian cancer detection by logical analysis of proteomic data. Proteomics 4(3), 766–783 (2004)
Antoniou, A., et al.: Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: A combined analysis of 22 studies. American Journal of Human Genetic 72, 1117–1130 (2003)
Bastide, Y., et al.: Mining non-redundant association rules using frequent closed itemsets. In: Palamidessi, C., et al. (eds.) CL 2000. LNCS (LNAI), vol. 1861, pp. 972–986. Springer, Heidelberg (2000)
Berry, A., Bordat, J.P., Sigayret, A.: A local approach to cocept generation. Technical report, ISMIA/LIRMM (2004)
International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome 431, 931–945 (2004)
Ganter, B., Wille, R.: Formal Concept Analysis, Mathematical Foundations. Springer, Heidelberg (1999)
GeneChip®, Affymetrix, Santa Clara, California
King, M.-C., Marks, J.H., Mandell, J.B.: For The New York Breast Cancer Study Group. Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2 302, 643–646 (2003)
Kuznetsov, S.O.: Machine learning on the basis of formal concept analysis. Annotation and remote control 62(10), 1543–1564 (2001). Translated from Automatika i Telemekhanika, No. 10, 3–27 (2001)
Mann, G.J., et al.: The Kathleen Cuningham Concortium for Reasearch in Familial Breast Cancer. Analysis of cancer risk and BRCA1 and BRCA2 mutation prevalence in the kConFab familial breast cancer resource. Breast Cancer Research 8(1) (2006)
MATLAB®7(R14), The MathWorks, Natick, Massachusetts
Stumme, G., et al.: Computing iceberg concept lattices with TITANIC. Data Knowledge Engineering 42(2), 189–222 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Motameny, S., Versmold, B., Schmutzler, R. (2008). Formal Concept Analysis for the Identification of Combinatorial Biomarkers in Breast Cancer. In: Medina, R., Obiedkov, S. (eds) Formal Concept Analysis. ICFCA 2008. Lecture Notes in Computer Science(), vol 4933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78137-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-78137-0_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78136-3
Online ISBN: 978-3-540-78137-0
eBook Packages: Computer ScienceComputer Science (R0)