Multi-dimensional discovery of biomarker and phenotype complexes

BMC Bioinformatics

Proceedings

Jie Zhang¹  Tara B Borlawsky²  Philip RO Payne³  Kun Huang⁴  Abhisek Kundu⁵  Kristin Keen-Circle⁶ 

[1] Comprehensive Cancer Center Biomedical Informatics Shared Resources, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA;Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA;Center for Clinical and Translational Science, The Ohio State University, Suite 205, 376 West 10th Avenue, 43210, Columbus, Ohio, USA;Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA;Center for Clinical and Translational Science, The Ohio State University, Suite 205, 376 West 10th Avenue, 43210, Columbus, Ohio, USA;Comprehensive Cancer Center Biomedical Informatics Shared Resources, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA;Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA;Comprehensive Cancer Center Biomedical Informatics Shared Resources, The Ohio State University, 3190 Graves Hall, 333 West 10th Avenue, 43210, Columbus, Ohio, USA;Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Avenue, 43210, Columbus, Ohio, USA;Mt. Carmel College of Nursing, 127 South Davis Avenue, 43222, Columbus, OH, USA;

关键词: Chronic Lymphocytic Leukemia;    Resource Description Framework;    Conceptual Knowledge;    Unify Medical Language System;    Constructive Induction;   

DOI  :  10.1186/1471-2105-11-S9-S3

来源: Springer

【 摘 要 】

BackgroundGiven 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.ResultsIn 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.ConclusionsOur 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.

【 授权许可】

CC BY   
© Payne et al; licensee BioMed Central Ltd. 2010