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For: Lewis JE, Kemp ML. Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance. Nat Commun 2021;12:2700. [PMID: 33976213 DOI: 10.1038/s41467-021-22989-1] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Wang X, Dong Y, Zheng Y, Chen Y. Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective. J Genet Genomics 2021:S1673-8527(21)00169-7. [PMID: 34362682 DOI: 10.1016/j.jgg.2021.05.008] [Reference Citation Analysis]
2 Lee SM, Kim HU. Development of computational models using omics data for the identification of effective cancer metabolic biomarkers. Mol Omics 2021;17:881-93. [PMID: 34608924 DOI: 10.1039/d1mo00337b] [Reference Citation Analysis]
3 Gómez-Cebrián N, Domingo-Ortí I, Poveda JL, Vicent MJ, Puchades-Carrasco L, Pineda-Lucena A. Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments. Cancers (Basel) 2021;13:4544. [PMID: 34572770 DOI: 10.3390/cancers13184544] [Reference Citation Analysis]
4 Arjmand B, Hamidpour SK, Tayanloo-beik A, Goodarzi P, Aghayan HR, Adibi H, Larijani B. Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer. Front Genet 2022;13:824451. [DOI: 10.3389/fgene.2022.824451] [Reference Citation Analysis]
5 Yaneske E, Zampieri G, Bertoldi L, Benvenuto G, Angione C. Genome-scale metabolic modelling of SARS-CoV-2 in cancer cells reveals an increased shift to glycolytic energy production. FEBS Lett 2021;595:2350-65. [PMID: 34409594 DOI: 10.1002/1873-3468.14180] [Reference Citation Analysis]
6 Gonçalves AC, Richiardone E, Jorge J, Polónia B, Xavier CPR, Salaroglio IC, Riganti C, Vasconcelos MH, Corbet C, Sarmento-Ribeiro AB. Impact of cancer metabolism on therapy resistance - Clinical implications. Drug Resist Updat 2021;:100797. [PMID: 34955385 DOI: 10.1016/j.drup.2021.100797] [Reference Citation Analysis]
7 Jung HD, Sung YJ, Kim HU. Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells. Front Genet 2021;12:742902. [PMID: 34691155 DOI: 10.3389/fgene.2021.742902] [Reference Citation Analysis]
8 Xu Z, Chen H, Sun J, Mao W, Chen S, Chen M. Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence. Bioengineered 2021;12:11108-25. [PMID: 34738881 DOI: 10.1080/21655979.2021.2000122] [Reference Citation Analysis]
9 Yang H, Arif M, Yuan M, Li X, Shong K, Türkez H, Nielsen J, Uhlén M, Borén J, Zhang C, Mardinoglu A. A network-based approach reveals the dysregulated transcriptional regulation in non-alcoholic fatty liver disease. iScience 2021;24:103222. [PMID: 34712920 DOI: 10.1016/j.isci.2021.103222] [Reference Citation Analysis]
10 Sampaio M, Rocha M, Dias O. Exploring synergies between plant metabolic modelling and machine learning. Computational and Structural Biotechnology Journal 2022. [DOI: 10.1016/j.csbj.2022.04.016] [Reference Citation Analysis]
11 Read GH, Bailleul J, Vlashi E, Kesarwala AH. Metabolic response to radiation therapy in cancer. Mol Carcinog 2021. [PMID: 34961986 DOI: 10.1002/mc.23379] [Reference Citation Analysis]
12 Huang M, Zheng B, Cai T, Li X, Liu J, Qian C, Chen H. Machine–learning-enabled metasurface for direction of arrival estimation. Nanophotonics 2021;0. [DOI: 10.1515/nanoph-2021-0663] [Reference Citation Analysis]
13 Lombardo SD, Wangsaputra IF, Menche J, Stevens A. Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease. Genes 2022;13:764. [DOI: 10.3390/genes13050764] [Reference Citation Analysis]