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Defense: Roland Madidjim

Date:
Time:
1:00 pm
Zoom
MS Thesis Defense: Roland Madadjim

Join Zoom Meeting:
https://unl.zoom.us/j/91063596974

“Using an Integrative Machine Learning Approach to Study MicroRNA Regulation Networks in Pancreatic Cancer Progression”

Abstract: With advances in genomic discovery tools, recent biomedical research has pro duced a massive amount of data on post transcriptional regulations related to various transcription factors, microRNAs, lncRNAs, epigenetic modifications, and genetic variations. A question of interest arises in how to build a comprehensive gene regulation network based on this data so that it can capture different behavioral changes related to those molecules in the biological systems. To this end, conventional network inference techniques have encountered major challenges in terms of scalability and feasibility, given the complex dynamic changes of interactome in systems as chaotic as cancers, as well as the dramatic growth of heterogeneous data on this topic. This thesis addresses these challenges by proposing an integrative network learning model that can elucidate microRNA-gene interactions in cancer progression using genomic expression data. Specifically, I will present a high-throughput data processing pipeline for microRNA-gene interaction detection, a network learning approach that efficiently handles model fusion and data integration, as well as a list of 12 microRNA-gene conditional interactions identified based on the stages associated networks. Results from functional analysis have revealed novel pathological signaling and metabolic processes that shed light on the regulatory roles that microRNAs play in pancreatic cancer progression. We believe this integrative model can be a robust and effective discovery tool to further the understanding of key regulatory characteristics in complex biological systems.

Committee Members:
Dr. Juan Cui, Advisor
Dr. Jitender Deogun
Dr. Massimiliano Pierobon

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