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

Submission information
Submission Number: 7
Submission ID: 185466
Submission UUID: cb078977-dbf9-4421-a16d-cd3d8471d332

Created: Tue, 06/30/2026 - 13:13
Completed: Tue, 06/30/2026 - 13:29
Changed: Tue, 06/30/2026 - 13:29

Remote IP address: 10.208.28.16
Submitted by: Anonymous
Language: English

Is draft: No
First Name Mingyu
Middle Initial
Last Name Yang
Degree(s) Ph.D.
Position/Title/Career Status Associate Research Scientist
Organization Yale University
Organization Address New Haven
Email mingyu.yang@yale.edu
List of Additional Authors
Abstract Category Employing statistical, computational, and informatics tools, algorithms, and methods to integrate or analyze data
Abstract Keywords Spatial omics; Computational biology; Machine learning; Cancer genomics; Data integration
Abstract Title Computational Analysis of HTAN Spatial Multi-omics Data
Abstract 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.