NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #37

serial: '37'
sid: '145197'
uuid: b004aead-ce38-4a83-84b0-2b54f7a67f32
uri: /nci/ods-data-jamboree/abstractsubmissions
created: '1750724000'
completed: '1750724005'
changed: '1750724005'
in_draft: '0'
current_page: ''
remote_addr: 10.208.24.253
uid: '0'
langcode: en
webform_id: nci_office_of_data_sharing_abstr
entity_type: node
entity_id: '2107'
locked: '0'
sticky: '0'
notes: ''
metatag: meta
data:
  category: 'Employment of statistical methods or existing computational, mathematical, or informatics tools'
  degree_s_: Ph.D
  email: brian.capaldo@nih.gov
  first_name: Brian
  keywords_abstracts: 'linear modeling, xenograft'
  last_name: Capaldo
  middle_initial: ''
  organization: 'National Cancer Institute'
  organization_address:
    address: ''
    address_2: ''
    city: Rockville
    country: ''
    postal_code: ''
    state_province: ''
  other_please_specify_: ''
  summary: 'Xenograft models present a unique opportunity to study patient samples in an in vivo model, however, contamination of mouse reads can result in spurious interpretations of the data. Using linear mixed effect models, we can effectively remove influences of the mouse transcriptome on the human data. '
  title: 'Biomedical informatics specialist'
  ttile: 'Mixed effect modeling of human and mouse data in xenograft studies corrects for missed contamination or transcriptomic spillover'
  upload_abstract: '65606'