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This article is part of the supplement: Selected Proceedings of the 2010 AMIA Summit on Translational Bioinformatics

Open Access Proceedings

Multi-dimensional discovery of biomarker and phenotype complexes

Philip RO Payne123*, Kun Huang13, Kristin Keen-Circle4, Abhisek Kundu5, Jie Zhang3 and Tara B Borlawsky12

Author Affiliations

1 Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA

2 Center for Clinical and Translational Science, The Ohio State University, Suite 205, 376 West 10th Avenue, 43210, Columbus, Ohio, USA

3 Comprehensive Cancer Center Biomedical Informatics Shared Resources, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA

4 Mt. Carmel College of Nursing, 127 South Davis Avenue, 43222, Columbus, OH, USA

5 Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Avenue, 43210, Columbus, Ohio, USA

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BMC Bioinformatics 2010, 11(Suppl 9):S3  doi:10.1186/1471-2105-11-S9-S3

Published: 28 October 2010

Abstract

Background

Given the rapid growth of translational research and personalized healthcare paradigms, the ability to relate and reason upon networks of bio-molecular and phenotypic variables at various levels of granularity in order to diagnose, stage and plan treatments for disease states is highly desirable. Numerous techniques exist that can be used to develop networks of co-expressed or otherwise related genes and clinical features. Such techniques can also be used to create formalized knowledge collections based upon the information incumbent to ontologies and domain literature. However, reports of integrative approaches that bridge such networks to create systems-level models of disease or wellness are notably lacking in the contemporary literature.

Results

In response to the preceding gap in knowledge and practice, we report upon a prototypical series of experiments that utilize multi-modal approaches to network induction. These experiments are intended to elicit meaningful and significant biomarker-phenotype complexes spanning multiple levels of granularity. This work has been performed in the experimental context of a large-scale clinical and basic science data repository maintained by the National Cancer Institute (NCI) funded Chronic Lymphocytic Leukemia Research Consortium.

Conclusions

Our results indicate that it is computationally tractable to link orthogonal networks of genes, clinical features, and conceptual knowledge to create multi-dimensional models of interrelated biomarkers and phenotypes. Further, our results indicate that such systems-level models contain interrelated bio-molecular and clinical markers capable of supporting hypothesis discovery and testing. Based on such findings, we propose a conceptual model intended to inform the cross-linkage of the results of such methods. This model has as its aim the identification of novel and knowledge-anchored biomarker-phenotype complexes.