Publikationen von Z. Nikoloski

Eng, R.C.; Schneider, R.; Matz, T.; Carter, R.; Ehrhardt, D. W.; Jönsson, H.; Nikoloski, Z.; Sampathkumar, A.: KATANIN and CLASP function at different spatial scales to mediate microtubule response to mechanical stress in Arabidopsis cotyledons. Current Biology 31 (15), S. 3262 - 3274 (2021)

Nowak, J.; Eng, R.C.; Matz, T.; Waack, M.; Persson, S.; Sampathkumar, A.; Nikoloski, Z.: A network-based framework for shape analysis enables accurate characterization of leaf epidermal cells. Nature Communications 12 (1), 458 (2021)

Beleggia, R.; Omranian, N.; Holtz, Y.; Gioia, T.; Fiorani, F.; Nigro, F.M.; Pecchioni, N.; De Vita, P.; Schurr, U.; David, J.L. et al.; Nikoloski, Z.; Papa, R.: Comparative analysis based on transcriptomics and metabolomics data reveal differences between emmer and durum wheat in response to nitrogen starvation. International Journal of Molecular Sciences 22 (9), 4790 (2021)

Küken, A.; Wendering, P.; Langary, D.; Nikoloski, Z.: A structural property for reduction of biochemical networks. Scientific Reports 11 (1), 17415 (2021)

Mbebi, A. J.; Tong, H.; Nikoloski, Z.: L2,1-norm regularized multivariate regression model with applications to genomic prediction. Bioinformatics 37 (18), S. 2896 - 2904 (2021)

Omranian, N.; Angeleska, A.; Nikoloski, Z.: PC2P: parameter-free network-based prediction of protein complexes. Bioinformatics 37 (1), S. 73 - 81 (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)

Tong, H.; Nikoloski, Z.: Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. Journal of Plant Physiology 257, 153354 (2021)

Nowak, J.; Gennermann, K.; Persson, S.; Nikoloski, Z.: CytoSeg 2.0: automated extraction of actin filaments. Bioinformatics 36 (9), S. 2950 - 2951 (2020)

Clemente-Moreno, M.; Omranian, N.; Sáez, P.; Figueroa, C.; Del-Saz, N.; Elso, M.; Poblete, L.; Orf, I.; Cuadros-Inostroza, A.; Cavieres, L.A. et al.; Bravo, L.; Fernie, A. R.; Ribas-Carbó, M.; Flexas, J.; Nikoloski, Z.; Brotman, Y.; Gago, J.: Low temperature tolerance of the Antarctic species Deschampsia antarctica: a complex metabolic response associated with nutrient remobilization. Plant, Cell and Environment 43 (6), S. 1376 - 1393 (2020)

Correa, S. M.; Alseekh, S.; Atehortúa, L.; Brotman, Y.; Ríos-Estepa, R.; Fernie, A. R.; Nikoloski, Z.: Model-assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways. The Plant Journal (angenommen)

Correa, S. M.; Fernie, A. R.; Nikoloski, Z.; Brotman, Y.: Towards model-driven characterization and manipulation of plant lipid metabolism. Progress in Lipid Research 80, 101051 (2020)

Küken, A.; Gennermann, K.; Nikoloski, Z.: Characterization of maximal enzyme catalytic rates in central metabolism of Arabidopsis thaliana. The Plant Journal 103 (6), S. 2168 - 2177 (2020)

Lyall, R.; Nikoloski, Z.; Gechev, T.: Comparative analysis of ros network genes in extremophile eukaryotes. International Journal of Molecular Sciences 21 (23), S. 1 - 27 (2020)

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

Robaina-Estévez, S.; Nikoloski, Z.: Flux-based hierarchical organization of Escherichia coli’s metabolic network. PLoS Computational Biology 16 (4), e1007832 (2020)

Tong, H.; Küken, A.; Nikoloski, Z.: Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth. Nature Communications 11 (1), 2410 (2020)

Angeleska, A.; Nikoloski, Z.: Coherent network partitions. Discrete applied mathematics 266, S. 283 - 290 (2019)