NCI Data Jamboree (Project Abstract Submission): Submission #7

serial: '7'
sid: '185466'
uuid: cb078977-dbf9-4421-a16d-cd3d8471d332
uri: /nci/datajamboree/abstractsubmission
created: '1782839581'
completed: '1782840583'
changed: '1782840583'
in_draft: '0'
current_page: ''
remote_addr: 10.208.28.16
uid: '0'
langcode: en
webform_id: nci_data_jamboree_abstracts
entity_type: node
entity_id: '2272'
locked: '0'
sticky: '0'
notes: ''
metatag: meta
data:
  list_of_additional_authors: {  }
  category: 'Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data'
  degree_s_: Ph.D.
  email: mingyu.yang@yale.edu
  first_name: Mingyu
  keywords_abstracts: 'Spatial omics; Computational biology; Machine learning; Cancer genomics; Data integration'
  last_name: Yang
  middle_initial: ''
  organization: 'Yale University'
  organization_address:
    address: ''
    address_2: ''
    city: 'New Haven'
    country: ''
    postal_code: ''
    state_province: ''
  summary: |-
    I have over 15 years of experience in bioinformatics, developing computational methods and analytical pipelines for large-scale sequencing data across cancer and other human diseases. My research has evolved from bulk genomics and transcriptomics to single-cell sequencing and, more recently, spatial multi-omics. At Yale University, I have been involved in developing computational methods for analyzing spatial transcriptomics and proteomics data, with a particular interest in applying AI and machine learning to understand tumor heterogeneity and the tumor microenvironment.

    I would like to participate in the HTAN Data Jamboree because I am passionate about cancer research and believe that the extensive HTAN datasets provide an exceptional opportunity to develop new computational methods by reusing existing high-quality data. I look forward to collaborating with researchers from diverse backgrounds, exchanging ideas, and learning from experts in cancer biology, spatial omics, and data science. I believe that combining complementary expertise will inspire innovative approaches that would be difficult to develop independently.

    Through the Jamboree, I hope to identify an important computational challenge that can benefit from statistical and machine learning approaches and to brainstorm a novel analytical framework with potential collaborators. My goal is to leave the event with a well-defined project concept and a collaborative team that can continue working together beyond the Jamboree. Ultimately, I hope this effort will lead to new computational methods, open-source software, and publications that help maximize the value of HTAN data for the broader cancer research community.
  title: 'Associate Research Scientist'
  ttile: 'Computational Analysis of HTAN Spatial Multi-omics Data'