Summary of project PR002500
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 PR002500. The data can be accessed directly via it's Project DOI: 10.21228/M8GV7H This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Project ID: | PR002500 |
| Project DOI: | doi: 10.21228/M8GV7H |
| Project Title: | A machine learning framework to predict cancer metabolomics from gene expression data |
| Project Summary: | Metabolomics provides a direct functional readout of a tumor’s physiology. Yet, it is lagging behind other omics technologies in facilitating disease monitoring and prognostication. This stems partly from the scarcity of large-scale metabolomic studies, but also the analytical complexities of detecting diverse metabolites with varying physicochemical properties and concentrations. To address this, we developed a machine learning framework using both tumor tissue and cell line samples across multiple cancer types that allows prediction of metabolomics from gene expression data. Two different model types were selected and trained for tissues and cell lines with their generalization capacity validated on independent cohorts, accurately predicting as high as 70-80% of tested metabolites. This work offers a scalable and efficient machine learning pipeline to determine metabolic from transcriptomic signatures, opening avenues to reconstruct and study the metabolic landscape of samples across novel and existing datasets lacking direct metabolomics measurements. |
| Institute: | The Institute of Cancer Research London |
| Department: | Cell and Molecular Biology |
| Laboratory: | Signalling and Cancer Metabolism |
| Last Name: | Poulogiannis |
| First Name: | George |
| Address: | 237 Fulham Road, LONDON, London, SW3 6JB, United Kingdom |
| Email: | george.poulogiannis@icr.ac.uk |
| Phone: | +442071535347 |
| Funding Source: | Work in the GP lab was supported by UK Research and Innovation (MR/W012030/1 and MC_PC_MR/X013715/1). |
Summary of all studies in project PR002500
| Study ID | Study Title | Species | Institute | Analysis(* : Contains Untargted data) | Release Date | Version | Samples | Download(* : Contains raw data) |
|---|---|---|---|---|---|---|---|---|
| ST003995 | A machine learning framework to predict cancer metabolomics from gene expression data | Homo sapiens | The Institute of Cancer Research London | MS | 2025-09-30 | 1 | 25 | Uploaded data (4.5G)* |
