NCI Data Jamboree (Project Abstract Submission): Submission #59

serial: '59'
sid: '189190'
uuid: 4a993c69-88b0-4dfa-b888-4fd4374fe59b
uri: /nci/datajamboree/abstractsubmission
created: '1785193025'
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data:
  list_of_additional_authors:
    - add_author_letters: ''
      affiliation: .
      first_name: .
      last_name: .
  category: 'Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data'
  degree_s_: 'B.S. Molecular and Cellular Biology'
  email: rawan.elshobaky@cuanschutz.edu
  first_name: Rawan
  keywords_abstracts: 'multi-omics, risk prediction, precision medicine, computational genomics'
  last_name: Elshobaky
  middle_initial: ''
  organization: 'University of Colorado Anschutz'
  organization_address:
    address: ''
    address_2: ''
    city: 'Aurora, Colorado'
    country: ''
    postal_code: ''
    state_province: ''
  summary: |-
    I am a Ph.D. student in Human Medical Genomics at the University of Colorado Anschutz. I earned my B.S. in Molecular and Cellular Biology from Johns Hopkins with minors in Computer Science and Bioethics. My doctoral research, under the mentorship of Drs. David Conti and Milton Pividori, focuses on developing computational methods to improve the performance, interpretability, and portability of polygenic risk scores (PRS) across populations. Specifically, I use data from the UK Biobank to develop and evaluate risk scores based on predicted gene expression data and utilize functional information with Bayesian fine-mapping methods to prioritize likely causal variants and interpret genetic associations.
    I am excited to participate in the NCI Data Jamboree because its team-based, interdisciplinary format provides a unique opportunity to work alongside researchers with complementary expertise to address challenges in cancer data integration and analysis. My research is motivated by the need to develop computational methods that perform reliably across populations, recognizing that robust genetic prediction depends on developing and evaluating methods using datasets that are representative of the populations they are intended to serve. Therefore, I am particularly interested in projects involving the All of Us Research Program, because its large, broadly representative participant cohort, combined with genomic, multi-omic, environmental, and longitudinal clinical data, provides a unique opportunity to evaluate methods that generalize robustly across populations. 
    Through the jamboree, I hope to gain practical experience in effectively accessing and leveraging large-scale cancer resources, such as All of Us, and contribute to incorporating multimodal data into reproducible computational workflows. I also hope to bring this expertise back to the University of Colorado Anschutz Department of Biomedical Informatics and help facilitate the adoption of these resources in future collaborative research. Finally, I look forward to building lasting collaborations with other participants and continuing these interactions beyond the jamboree.
  title: 'PhD student'
  ttile: 'Integrating Multimodal Data for Improving Risk Prediction'