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Journal Article (8)

Journal Article
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).
Journal Article
Seep, L., Razaghi-Moghadam, Z. & Nikoloski, Z. Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis. Scientific Reports 11, (2021).
Journal Article
Tong, H., Küken, A., Razaghi-Moghadam, Z. & Nikoloski, Z. Characterization of effects of genetic variants via genome-scale metabolic modelling. Cellular and Molecular Life Sciences 78, 5123–5138 (2021).
Journal Article
Razaghi-Moghadam, Z. & Nikoloski, Z. GeneReg: A constraint-based approach for design of feasible metabolic engineering strategies at the gene level. Bioinformatics (2020). doi:10.1093/bioinformatics/btaa996
Journal Article
Razaghi-Moghadam, Z. & Nikoloski, Z. Supervised Learning of Gene Regulatory Networks. Current Protocols in Plant Biology 5, (2020).
Journal Article
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).
Journal Article
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).
Journal Article
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).

Conference Paper (1)

Conference Paper
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
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