Multi-dimensional discovery of biomarker and phenotype complexes

Philip R.O. Payne, Kun Huang, Kristin Keen-Circle, Abhisek Kundu, Jie Zhang, Tara B. Borlawsky

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish (US)
Article numberS3
JournalBMC Bioinformatics
Volume11
Issue numberSUPPL. 9
DOIs
StatePublished - Oct 28 2010
Externally publishedYes

Fingerprint

Biomarkers
Phenotype
Granularity
Literature
Genes
Bridge approaches
Translational Medical Research
National Cancer Institute (U.S.)
Gene Regulatory Networks
Gene
B-Cell Chronic Lymphocytic Leukemia
Multidimensional Model
Leukemia
Conceptual Model
Linkage
Repository
Healthcare
Experiment
Ontology
Delivery of Health Care

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Payne, P. R. O., Huang, K., Keen-Circle, K., Kundu, A., Zhang, J., & Borlawsky, T. B. (2010). Multi-dimensional discovery of biomarker and phenotype complexes. BMC Bioinformatics, 11(SUPPL. 9), [S3]. https://doi.org/10.1186/1471-2105-11-S9-S3

Multi-dimensional discovery of biomarker and phenotype complexes. / Payne, Philip R.O.; Huang, Kun; Keen-Circle, Kristin; Kundu, Abhisek; Zhang, Jie; Borlawsky, Tara B.

In: BMC Bioinformatics, Vol. 11, No. SUPPL. 9, S3, 28.10.2010.

Research output: Contribution to journalArticle

Payne, PRO, Huang, K, Keen-Circle, K, Kundu, A, Zhang, J & Borlawsky, TB 2010, 'Multi-dimensional discovery of biomarker and phenotype complexes', BMC Bioinformatics, vol. 11, no. SUPPL. 9, S3. https://doi.org/10.1186/1471-2105-11-S9-S3
Payne PRO, Huang K, Keen-Circle K, Kundu A, Zhang J, Borlawsky TB. Multi-dimensional discovery of biomarker and phenotype complexes. BMC Bioinformatics. 2010 Oct 28;11(SUPPL. 9). S3. https://doi.org/10.1186/1471-2105-11-S9-S3
Payne, Philip R.O. ; Huang, Kun ; Keen-Circle, Kristin ; Kundu, Abhisek ; Zhang, Jie ; Borlawsky, Tara B. / Multi-dimensional discovery of biomarker and phenotype complexes. In: BMC Bioinformatics. 2010 ; Vol. 11, No. SUPPL. 9.
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