NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #12

serial: '12'
sid: '144980'
uuid: 6f1093b0-562b-42c3-96e7-6355edda7042
uri: /nci/ods-data-jamboree/abstractsubmissions
created: '1750268392'
completed: '1750268594'
changed: '1750268594'
in_draft: '0'
current_page: ''
remote_addr: 10.208.28.250
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: 'Development or refinement of analysis pipelines or AI/ML algorithms'
  degree_s_: master
  email: tinh.nguyen@nih.gov
  first_name: trinh
  keywords_abstracts: 'multi omics, unsupervised clustering, pathway analysis, subgroups'
  last_name: nguyen
  middle_initial: ''
  organization: NIH/NCI/CGBB
  organization_address:
    address: ''
    address_2: ''
    city: 'Rockville, MD'
    country: ''
    postal_code: ''
    state_province: ''
  other_please_specify_: ''
  summary: 'To recognize clinically important intrinsic cancer subtypes, it is important to combine multi-omics datasets to find multi-omics clusters, and the interrelationships between biomolecules and their functions. Here, we will use unsupervised clustering using information from at least two data types to search for subgroups of interests. Next, we will use our Multi-omics Pathways Workflow, an automated Multi-omics Workflow on the Cancer Genomics Cloud to search for activated molecular pathways for each subgroup.  The omics data could include copy number alterations, transcriptomics data, proteomics and phospho-proteomics data.The distinct pathways for subgroups found by unsupervised clustering will be displayed graphically (e.g., in heatmaps) to facilitate interpretation with clinical data.'
  title: Bioinformatician
  ttile: 'Molecular Characterization of childhood cancer subtypes through Multi-Omics Clustering'
  upload_abstract: '65536'