Publikationen von Z. Razaghi-Moghadam

Seyis, M.; Razaghi-Moghadam, Z.; Nikoloski, Z.: Flux-sum coupling analysis of metabolic network models. PLOS Computational Biology 21 (4), e1012972 (2025)

Razaghi-Moghadam, Z.; Soleymani Babadi, F.; Nikoloski, Z.: Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes. PLOS Computational Biology 20 (11), e1012576 (2024)

Habibpour , M.; Razaghi-Moghadam, Z.; Nikoloski, Z.: Machine learning of metabolite-protein interactions from model-derived metabolic phenotypes. NAR: genomics and bioinformatics 6 (3), lqae114 (2024)

Habibpour, M.; Razaghi-Moghadam, Z.; Nikoloski, Z.: Prediction and integration of metabolite-protein interactions with genome-scale metabolic models. Metabolic Engineering 82, S. 216 - 224 (2024)

Bezold, F.; Scheffer, J.; Wendering, P.; Razaghi-Moghadam, Z.; Trauth, J.; Pook, B.; Nußhär, H.; Hasenjäger, S.; Nikoloski, Z.; Essen, L.-O. et al.; Taxis, C.: Optogenetic control of Cdc48 for dynamic metabolic engineering in yeast. Metabolic Engineering 79, S. 97 - 107 (2023)

Hashemi, S.; Razaghi-Moghadam, Z.; Nikoloski, Z.: Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony. PLoS Computational Biology 19 (9), e1011489 (2023)

Babadi, F. S.; Razaghi-Moghadam, Z.; Zare-Mirakabad, F.; Nikoloski, Z.: Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling. Bioinformatics advances 3 (1), vbad098 (2023)

Wendering, P.; Arend, M.; Razaghi-Moghadam, Z.; Nikoloski, Z.: Data integration across conditions improves turnover number estimates and metabolic predictions. Nature Communications 14, 1485 (2023)

Hashemi, S.; Razaghi-Moghadam, Z.; Laitinen, R.A.E.; Nikoloski, Z.: Relative flux trade-offs and optimization of metabolic network functionalities. Computational and Structural Biotechnology Journal 20, S. 3963 - 3971 (2022)

Hashemi, S.; Razaghi-Moghadam, Z.; Nikoloski, Z.: Identification of flux trade-offs in metabolic networks. Scientific Reports 11 (1), 23776 (2021)

Xu, R.; Razaghi-Moghadam, Z.; Nikoloski, Z.: Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli. Bioinformatics 37 (21), S. 3848 - 3855 (2021)

Razaghi-Moghadam, Z.; Nikoloski, Z.: GeneReg: a constraint-based approach for design of feasible metabolic engineering strategies at the gene level. Bioinformatics 37 (12), S. 1717 - 1723 (2021)

Pries, C.; Razaghi-Moghadam, Z.; Kopka, J.; Nikoloski, Z.: Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism. Scientific Reports 11 (1), 4787 (2021)

Razaghi-Moghadam, Z.; Sokolowska, E.; Sowa, M.A.; Skirycz, A.; Nikoloski, Z.: Combination of network and molecule structure accurately predicts competitive inhibitory interactions. Computational and Structural Biotechnology Journal 19, S. 2170 - 2178 (2021)

Seep, L.; Razaghi-Moghadam, Z.; Nikoloski, Z.: Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis. Scientific Reports 11, 8544 (2021)

Razaghi-Moghadam, Z.; Nikoloski, Z.: Supervised Learning of Gene Regulatory Networks. Current Protocols in Plant Biology 5 (2), e20106 (2020)

Razaghi-Moghadam, Z.; Namipashaki, A.; Farahmand, S.; Ansari-Pour, N.: Systems genetics of nonsyndromic orofacial clefting provides insights into its complex aetiology. European Journal of Human Genetics 27 (2), S. 226 - 234 (2019)

Shamsizadeh, S.; Goliaei, S.; Razaghi-Moghadam, Z.: CAMIRADA: Cancer microRNA association discovery algorithm, a case study on breast cancer. Journal of biomedical informatics 94, 103180 (2019)

Ashtiani, M.; Salehzadeh-Yazdi, A.; Razaghi-Moghadam, Z.; Hennig, H.; Wolkenhauer, O.; Mirzaie, M.; Jafari, M.: A systematic survey of centrality measures for protein-protein interaction networks. BMC Systems Biology 12 (1), 80 (2018)

Razaghi-Moghadam, Z.; Masoudi-Nejad, A.; Nowzari-Dalini, A.: ParaKavosh: A Parallel Algorithm for Finding Biological Network Motifs. In: 2020 11th International Conference on Information and Knowledge Technology (IKT), S. 45 - 49. (2020)