CRII: III: Computational Methods to Explore the Role of Post-transcriptional Regulation in Cancer

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This project will develop computational methods to investigate the role of post-transcriptional regulation in cancer patients using both high-throughput sequencing data and molecular networks to improve the understanding of cancer and assess the cause of the disease in a patient. Deregulation of gene expression is a hallmark of the human tumor cells. Post-transcriptional regulation is a pervasive mechanism in the relation of most human genes; its implication in cancer is only beginning to be appreciated. The central hypothesis underlying this project is that by considering post-transcriptional regulation events, estimated protein expressions can provide more accurate molecular signatures to detect complex disease mechanisms, compared to mRNA expressions. This project will study post-transcriptional regulation, with the goal of generating computational methods to predict the changes of protein expression level without doing large-scale proteomics experiments. The outcomes of the proposed research will lower the barriers for interacting with analyzing high-dimensional genomic profiles and cut time and costs spent on biomedical research. The new methods will enable biologists and biomedical researchers to perform comprehensive analysis with high-throughput sequencing data and biological networks together to investigate the impact of post-transcriptional regulation in certain cancer types.This project to use a variety of sequences modalities has three phases: 1) Develop advanced machine learning methods to identify the genome-wide alternative polyadenylation (APA) events in the 3' untranslated region (3'UTR) of the mRNAs between cancer patients and matched normal samples with RNA-Seq data. 2) Develop biologically motivated graph-based learning models and efficient scalable algorithms to estimate the protein expression levels with microRNA-mRNA interaction networks and 3'UTR-APA events. 3) Investigate the prognostic power of the identified APA and estimated protein expressions compared to the canonical genomic features such as mRNA expression, mutation, and copy number variations. The project not only employs interpretable computational models which capture the causal relations in post-transcriptional regulation of protein-coding genes to better understand complex human diseases, but also develops unique machine learning methods and optimization techniques for computer science research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.