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

Submission information
Submission Number: 63
Submission ID: 189204
Submission UUID: 4fd1609c-23fa-4a00-9667-2f0c7120520e

Created: Mon, 07/27/2026 - 23:11
Completed: Mon, 07/27/2026 - 23:11
Changed: Mon, 07/27/2026 - 23:11

Remote IP address: 10.208.24.192
Submitted by: Anonymous
Language: English

Is draft: No
First Name Avani
Middle Initial
Last Name Patel
Degree(s) N.A.
Position/Title/Career Status
Organization Animal Genome Institute
Organization Address Palmyra
Email info@animalgenomeinstitute.org
List of Additional Authors
  • First Name: Yuka
    Last Name: Imamura
    Affiliation: Animal Genome Institute
Abstract Category Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data
Abstract Keywords
Abstract Title Cross-Species Signature Scoring and Risk Assessment Modeling for Human and Canine Osteosarcoma
Abstract 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.