NCI Data Jamboree (Project Abstract Submission): Submission #52

serial: '52'
sid: '189162'
uuid: 9f6d2015-a5bc-43fa-820b-70969dbd21f7
uri: /nci/datajamboree/abstractsubmission
created: '1785181019'
completed: '1785181434'
changed: '1785181434'
in_draft: '0'
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langcode: en
webform_id: nci_data_jamboree_abstracts
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entity_id: '2272'
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notes: ''
metatag: meta
data:
  list_of_additional_authors: {  }
  category: 'Evaluating data quality for reproducibility and AI-readiness'
  degree_s_: 'M.S in Data Science'
  email: sarasing@uic.edu
  first_name: Saransh
  keywords_abstracts: 'AI-readiness; data quality; Cancer Research Data Commons; multimodal oncology data; data leakage'
  last_name: Singh
  middle_initial: ''
  organization: 'University of Illinois Cancer Center'
  organization_address:
    address: ''
    address_2: ''
    city: Chicago
    country: ''
    postal_code: ''
    state_province: ''
  summary: |-
    I am a confirmed member of the ready-to-go team for "AI-Readiness Scorecard: A Task-Relative Profiler for Multimodal Cancer Research Data Commons Datasets" (Lead: Nikita Thakur, UI Cancer Center) and am not seeking assignment to another project.
    My contribution will be the oncology domain pack: building the oncology.yaml required-variable inventory per AI task, biomarker panel, and ICD-O/mCODE/AJCC staging mappings, then validating that TCGA-BRCA clinical fields actually populate them, the layer where clinical judgment determines whether a dataset is truly usable for a given modeling task.
    My background aligns directly. As a Data Scientist in Oncology Informatics at the University of Illinois Cancer Center, I own real-world evidence analytics for CancerLinQ/RWD360, including a 31,200-patient Stage I–III lung cancer cohort and a 60-patient ALK+ cohort, where I standardized fragmented biomarker semantics and caught a 225-to-60 patient misclassification before manuscript submission. I also spearheaded our Precision Oncology Dashboard, reconciling Tempus NGS data and cohort counts across disjoint oncology databases.
    This gives me hands-on fluency in the exact failure modes this project targets, inconsistent staging fields, biomarker misclassification, cross-repository semantic drift  and I'm looking forward to building this out with the team.
  title: 'Research Data Analyst'
  ttile: 'Project Seeker - joining "AI-Readiness Scorecard: A Task-Relative Profiler for Multimodal Cancer Research Data Commons Datasets"'