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A Probabilistic Approach for Automated Discovery of Biomarkers using Expression Data from Microarray or RNA-Seq Datasets

Sundaramurthy, Gopinath

Abstract Details

2016, PhD, University of Cincinnati, Medicine: Systems Biology and Physiology.
The response to perturbations in cellular systems is governed by a large number of molecular circuits that coalesce into a complex network. In complex diseases, the breakdown of cellular components is brought about by multiple molecular and environmental perturbations. While individual signatures of cellular components might vary significantly among clinical patients, commonality in signs and symptoms of disease progression is a compelling indicator that key cellular sub-processes follow similar trajectories? -. Our approach aims for an enhanced understanding of the effect of disease perturbations on the cell by developing an automated platform that assigns more significance to changes that occur at the sub-network level – focusing on genes that are “wired” together and change together. The platform that we have developed is motivated by the study of concomitant expression changes in sub-networks. The analysis by our platform produces a small subset of signaling and regulatory genes that are wired together and change together beyond random chance. In order to evaluate the effectiveness of our platform in producing subsets that can distinguish diseases and disease-subtypes, we used publicly available RNA-Seq and microarray breast cancer expression datasets. Each dataset was analyzed independently using our platform and the disease related sub-network perturbations among breast cancer subtypes were identified. The resulting subset was subjected to standard multi-way classification and predictions based on our approach were compared with PAM50 predictions. Biomarkers identified from the microarray and RNA-Seq dataset reproduced the PAM50 classification with 100% and 80% agreement respectively despite having only 10% of genes common with the PAM50. This proof-of-concept analysis using breast cancer datasets is indicative of the platform’s stable cross-validation results. This platform can potentially be used for automated and unbiased computational discovery of disease related genes. Our results suggest that probabilistic and automated approaches may offer a powerful complement to existing approaches by providing an unbiased initial screen.
Steven Kleene, Ph.D. (Committee Chair)
Judith| Heiny, Ph.D. (Committee Member)
Anil Jegga, D.V.M. (Committee Member)
Jaroslaw Melle, Ph.D. (Committee Member)
Yana Zavros, Ph.D. (Committee Member)
195 p.

Recommended Citations

Citations

  • Sundaramurthy, G. (2016). A Probabilistic Approach for Automated Discovery of Biomarkers using Expression Data from Microarray or RNA-Seq Datasets [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1459528594

    APA Style (7th edition)

  • Sundaramurthy, Gopinath. A Probabilistic Approach for Automated Discovery of Biomarkers using Expression Data from Microarray or RNA-Seq Datasets. 2016. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1459528594.

    MLA Style (8th edition)

  • Sundaramurthy, Gopinath. "A Probabilistic Approach for Automated Discovery of Biomarkers using Expression Data from Microarray or RNA-Seq Datasets." Doctoral dissertation, University of Cincinnati, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1459528594

    Chicago Manual of Style (17th edition)