NCI Data Jamboree (Project Abstract Submission): Submission #63

serial: '63'
sid: '189204'
uuid: 4fd1609c-23fa-4a00-9667-2f0c7120520e
uri: /nci/datajamboree/abstractsubmission
created: '1785208281'
completed: '1785208281'
changed: '1785208281'
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langcode: en
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metatag: meta
data:
  list_of_additional_authors:
    - add_author_letters: ''
      affiliation: 'Animal Genome Institute'
      first_name: Yuka
      last_name: Imamura
  category: 'Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data'
  degree_s_: N.A.
  email: info@animalgenomeinstitute.org
  first_name: Avani
  keywords_abstracts: ''
  last_name: Patel
  middle_initial: ''
  organization: 'Animal Genome Institute'
  organization_address:
    address: ''
    address_2: ''
    city: Palmyra
    country: ''
    postal_code: ''
    state_province: ''
  summary: "Osteosarcoma is a genetically complex malignancy. While human and canine osteosarcomas share significant molecular similarities, integrating datasets across species and sequencing platforms remains a technical challenge. Overcoming these hurdles is crucial for identifying conserved oncological targets and improving the AI-readiness of comparative genomic data for the broader research community. We aim to develop and validate a robust computational framework for cross-species data integration, specifically focusing on continuous signature scoring models for human and canine osteosarcoma. By utilizing publicly available human osteosarcoma datasets (such as TARGET-OS) and public canine cohorts (such as NCI's DOG² cohort), we will employ expression data scaling techniques, cross-platform normalization, and continuous signature scoring algorithms to harmonize the disparate matrices. Ultimately, we aim to leverage these validated models to develop a cross-species risk assessment application to support clinical decision-making. "
  title: ''
  ttile: 'Cross-Species Signature Scoring and Risk Assessment Modeling for Human and Canine Osteosarcoma'