Published November 6, 2022
| Version 4
Dataset
Open
Prediction of metabolites associated with somatic mutations in cancers
Authors/Creators
- 1. Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- 2. Department of Genomic Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea
- 3. Department of Urology, Seoul National University College of Medicine, and Seoul National University Hospital, Seoul 03080, Republic of Korea
- 4. Department of Internal Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea
Description
- GEMs_AML: 16 acute myeloid leukemia (AML) patient-specific genome-scale metabolic models (GEMs) reconstructed using their corresponding RNA-seq data and Recon 2M.2
- GEMs_PCAWG: 943 cancer patient-specific GEMs for 24 different cancer types reconstructed using the Pan-Cancer Analysis of Whole Genomes (PCAWG) RNA-seq data and generic human GEM 'Recon 2M.2'
- GEMs_RCC: 20 renal cell carcinoma (RCC) patient-specific GEMs reconstructed using their corresponding RNA-seq data and Recon 2M.2
- GEMs_TCGA_LAML: 113 AML patient-specific GEMs reconstructed using The Cancer Genome Atlas (TCGA) LAML RNA-seq data and Recon 2M.2
Notes
This study was supported by the Project of Promoting Inclusive Growth through Artificial Intelligence and Blockchain Technology and Diffusion of Precision Medicine (1711125351) funded by the Ministry of Science and ICT through KAIST's Korea Policy Center for the Fourth Industrial Revolution. This work was also supported by the KAIST Cross-Generation Collaborative Lab project of the Ministry of Science and ICT through the National Research Foundation of Korea, and by Kwon Oh-Hyun Assistant Professor fund of the KAIST Development Foundation.