Publikationen von Z. Razaghi-Moghadam

Seyis, M., Razaghi-Moghadam, Z. & Nikoloski, Z. Flux-sum coupling analysis of metabolic network models. PLOS Computational Biology 21, (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, (2024).

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

Habibpour, M., Razaghi-Moghadam, Z. & Nikoloski, Z. Prediction and integration of metabolite-protein interactions with genome-scale metabolic models. Metabolic Engineering 82, 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. & Taxis, C. Optogenetic control of Cdc48 for dynamic metabolic engineering in yeast. Metabolic Engineering 79, 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, (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, (2023).

Wendering, P., Arend, M., Razaghi-Moghadam, Z. & Nikoloski, Z. Data integration across conditions improves turnover number estimates and metabolic predictions. Nature Communications 14, (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, 3963–3971 (2022).

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

Razaghi-Moghadam, Z. & Nikoloski, Z. Supervised Learning of Gene Regulatory Networks. Current Protocols in Plant Biology 5, (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, 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, (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, (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) 45–49 (2020). doi:10.1109/IKT51791.2020.9345641