Summary of project PR002537
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR002537. The data can be accessed directly via it's Project DOI: 10.21228/M8Q839 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Project ID: | PR002537 |
| Project DOI: | doi: 10.21228/M8Q839 |
| Project Title: | Patient-centered artificial intelligence platform for endometrial cancer risk stratification using clinical and molecular multi-omics data |
| Project Summary: | Endometrial cancer (ENDOM) prevention remains challenging, creating an urgent need for better risk stratification tools. We developed a patient-centered bimodal multilevel endometrial cancer (2M-EC) predictive platform that integrates clinically accessible data with multi-biofluid multi-omics data. Our study established the MBF-ED cohort (n=531), collecting comprehensive clinical data and multi-dimensional body fluid samples. We processed these using a unique analytical pipeline: (1) simplifying clinical variables through empirical and data-driven methods, (2) extracting ENDOM-specific MS features using machine learning, and (3) developing an innovative bimodal AI architecture that fuses 2D MS omics matrices with 1D clinical vectors. The resulting patient-centered 2M-EC predictive platform provides real-time, interpretable risk stratification through an online interface. The advantages of it include overcoming single-marker limitations via multimodal integration, combining molecular depth with clinical practicality and scalable design adaptable to both resource-limited and advanced healthcare settings. This work demonstrates how AI can bridge cutting-edge molecular profiling with routine clinical practice, offering a new paradigm for patient-centered cancer risk assessment. |
| Institute: | Fudan University |
| Department: | Chemistry department |
| Laboratory: | LiangQiao lab |
| Last Name: | DANDAN |
| First Name: | LI |
| Address: | Fudan University |
| Email: | oceanddl@sina.com |
| Phone: | 18061019632 |
Summary of all studies in project PR002537
| Study ID | Study Title | Species | Institute | Analysis(* : Contains Untargted data) | Release Date | Version | Samples | Download(* : Contains raw data) |
|---|---|---|---|---|---|---|---|---|
| ST004047 | Artificial intelligence platform for endometrial cancer risk stratification using clinical and molecular multi-omics data | Homo sapiens | Fudan University | MS* | 2025-07-18 | 1 | 18 | Uploaded data (1.8G)* |
