Multi-dimensional discovery of biomarker and phenotype complexes

• Published in: BMC bioinformatics Vol. 11 Suppl 9; no. S9; p. S3
• Main Authors: Payne, Philip R O, Huang, Kun, Keen-Circle, Kristin, Kundu, Abhisek, Zhang, Jie, Borlawsky, Tara B
• Format: Journal Article
• Language: English
• Published: England BioMed Central 28.10.2010; BioMed Central Ltd; BMC
• Subjects: Automation; Biomarkers - analysis; Biomarkers - chemistry; Computational Biology - methods; Computer science; Electronic health records; Gene expression; Gene Regulatory Networks; Genotype & phenotype; Information management; Kinases; Leukemia; Medical prognosis; Methods; National Cancer Institute (U.S.); Phenotype; Proceedings; Software packages; Studies; Translational Medical Research - methods; United States; United States
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-11-S9-S3

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. 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. 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.