Childhood Cancer Data Initiative Annual Symposium (Abstract Registration): Submission #55

serial: '55'
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uri: /nci/ccdisymposium/abstract
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data:
  authors_:
    - add_author_degree: M.S.
      add_author_first_name: Zanyu
      add_author_last_name: Shi
      add_author_middle: ''
      add_author_organization: 'Department of Biostatistics & Health Data Science, Indiana University Fairbanks School of Public Health'
    - add_author_degree: M.S.
      add_author_first_name: Aishwarya
      add_author_last_name: Budhkar
      add_author_middle: ''
      add_author_organization: 'Department of Computer Science, Indiana University Luddy School of Informatics, Computing, and Engineering'
    - add_author_degree: M.D.
      add_author_first_name: Waqas
      add_author_last_name: Amin
      add_author_middle: ''
      add_author_organization: 'Indiana University School of Medicine'
    - add_author_degree: Ph.D.
      add_author_first_name: Karen
      add_author_last_name: Pollok
      add_author_middle: E.
      add_author_organization: 'Indiana University School of Medicine, IU Simon Comprehensive Cancer Center'
    - add_author_degree: Ph.D.
      add_author_first_name: Jing
      add_author_last_name: Su
      add_author_middle: ''
      add_author_organization: 'Indiana University School of Medicine, IU Simon Comprehensive Cancer Center'
    - add_author_degree: Ph.D.
      add_author_first_name: Kun
      add_author_last_name: Huang
      add_author_middle: ''
      add_author_organization: 'Indiana University School of Medicine, IU Simon Comprehensive Cancer Center'
  abstract: 'Advances in artificial intelligence (AI) are shifting the paradigm in precision medicine for pediatric cancer, including biomarker identification, drug discovery, and survival analysis. The Childhood Cancer Data Initiative (CCDI) ecosystem provides essential clinicogenomic data for deep learning in pediatric cancer research. To enable the efficient use of the CCDI resource for AI model training and implementation, we developed a generic graph AI platform, Graph Artificial Intelligence for Pediatric Oncology (GAIPO), as well as the standards of data, model, and pipeline, to streamline the use of CCDI clinicogenomics data of various data modalities from bulk and single-cell omics data to clinical information in AI development. GAIPO provides a comprehensive workflow: (1) Data fetching through CCDI Data Federation Resource API and cBioPortal API for clinical metadata, multi-omics, and spatial transcriptomics data; (2) Data modeling through mapping and harmonizing the fetched clinical and genomics data according to the CCDI data model; (3) Graph construction functionalities through GraphML from NetworkX; (4) Graph AI implementation of existing models; and (5) Post-analysis, such as vital feature selection and survival analysis. We demonstrate the capabilities of our GAIPO in treating two pediatric cancers, glioma and Wilms tumor, with potential applicability to other pediatric cancers. We apply graph AI models, MOGONET and PCGS, to integrate multi-omics data using explainable graph convolutional networks, allowing patient classification and biomarker identification. Notably, the new PCGS model identifies background-specific key features for biomarker discovery, risk group identification, and survival analysis in glioma and Wilms tumor, with potential applicability to other pediatric cancers. '
  abstract_title_: 'Graph Artificial Intelligence for Pediatric Oncology (GAIPO): a graph AI platform for precision oncology using pediatric clinical and omics data'
  email_address_: zanyshi@iu.edu
  institution_: 'Indiana University Indianapolis'
  presenting_author_: 'Zanyu Shi'