NLM IRP Seminar Schedule
UPCOMING SEMINARS
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Oct. 8, 2024 Jing Wang
Enhancing Heart Failure Prediction through LLM-backed Doctor Simulation -
Oct. 15, 2024 Tanvi Patel
Generative and Diagnostic Medical Imaging through AI -
Oct. 22, 2024 Lakshminarayan Iyer
TBD -
Oct. 29, 2024 Rezarta Islamaj
TBD -
Nov. 5, 2024 Max Burroughs
TBD
RECENT SEMINARS
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Oct. 1, 2024 Timothy Doerr
Electrostatics for biomolecular systems: ionic screening and more -
Sept. 24, 2024 Natalya Yutin
Virus discovery in the era of massive metagenomic sequencing -
Sept. 10, 2024 Diego Salazar Barreto
A phenome-wide association study to identify adverse events related with glucagon-like protein-1 agonists in Type 2 Diabetes cohort. -
July 23, 2024 Yu group
Yu Group Research Update -
July 18, 2024 Xiaofang Jiang
Jiang Lab research updates
Scheduled Seminars on May 21, 2024
Contact NLM_IRP_Seminar_Scheduling@mail.nih.gov with questions about this seminar.
Abstract:
To pave the road towards precision medicine in cancer, patients with similar biology ought to be grouped into same cancer subtypes. Utilizing high-dimensional multiomics datasets, integrative approaches have been developed to uncover cancer subtypes. Recently, Graph Neural Networks have been discovered to learn node embeddings utilizing node features and associations on graph-structured data. Some integrative prediction tools have been developed leveraging these advances on multiple networks with some limitations.
In this talk, a new method called SUPREME is introduced. SUPREME is a node classification framework, which integrates multiple data modalities on graph-structured data. On breast cancer subtyping, unlike existing tools, SUPREME generates patient embeddings from multiple similarity networks utilizing multiomics features and integrates them with raw features to capture complementary signals. On breast cancer subtype prediction tasks from three datasets, SUPREME outperformed other tools. SUPREME-inferred subtypes had significant survival differences, mostly having more significance than ground truth, and outperformed nine other approaches. These results suggest that with proper multiomics data utilization, SUPREME could demystify undiscovered characteristics in cancer subtypes that cause significant survival differences and could improve ground truth label, which depends mainly on one datatype. In addition, to show model-agnostic property of SUPREME, we applied it to two additional datasets and had a clear outperformance.