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Data Graphs for Linking Clinical Phenotype and Molecular Feature Space

Data Graphs for Linking Clinical Phenotype and Molecular Feature Space

Andreas Heinzel, Raul Fechete, Johannes Söllner, Paul Perco, Georg Heinze, Rainer Oberbauer, Gert Mayer, Arno Lukas, Bernd Mayer
Copyright: © 2012 |Volume: 1 |Issue: 1 |Pages: 15
ISSN: 2160-9586|EISSN: 2160-9594|EISBN13: 9781466615137|DOI: 10.4018/ijsbbt.2012010102
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MLA

Heinzel, Andreas, et al. "Data Graphs for Linking Clinical Phenotype and Molecular Feature Space." IJSBBT vol.1, no.1 2012: pp.11-25. http://doi.org/10.4018/ijsbbt.2012010102

APA

Heinzel, A., Fechete, R., Söllner, J., Perco, P., Heinze, G., Oberbauer, R., Mayer, G., Lukas, A., & Mayer, B. (2012). Data Graphs for Linking Clinical Phenotype and Molecular Feature Space. International Journal of Systems Biology and Biomedical Technologies (IJSBBT), 1(1), 11-25. http://doi.org/10.4018/ijsbbt.2012010102

Chicago

Heinzel, Andreas, et al. "Data Graphs for Linking Clinical Phenotype and Molecular Feature Space," International Journal of Systems Biology and Biomedical Technologies (IJSBBT) 1, no.1: 11-25. http://doi.org/10.4018/ijsbbt.2012010102

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Abstract

Omics profiling in translational clinical research has provided detailed molecular characterization of disease phenotypes. Integrating this molecular data space with clinical phenotype descriptors has triggered advancements regarding a systems view on disease, resulting in the concept of stratified medicine. The authors present a methodology for patient stratification by analyzing clinical and molecular information on a per-patient level represented as a data graph. This approach rests on linking patient specific clinical data and biomarker profiles with molecular functional units being derived by segmenting a human proteome interaction network. As a result patient strata are built holding sets of affected functional molecular units as common denominator. Annotation of such functional units on the level of associated diseases, biomarkers and drug targets allows reconciliation with respective clinical data for further improving the assignment of patients to specific strata. The authors finally discuss this approach in the light of adaptive clinical trials design and analysis.

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