Aleksej Zelezniak
Aleksej Zelezniak
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Cited by
Metabolic dependencies drive species co-occurrence in diverse microbial communities
A Zelezniak, S Andrejev, O Ponomarova, DR Mende, P Bork, KR Patil
Proceedings of the National Academy of Sciences 112 (20), 6449-6454, 2015
Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection
CB Messner, V Demichev, D Wendisch, L Michalick, M White, A Freiwald, ...
Cell systems 11 (1), 11-24. e4, 2020
Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies
M Tramontano, S Andrejev, M Pruteanu, M Klünemann, M Kuhn, ...
Nature microbiology 3 (4), 514-522, 2018
Functional metabolomics describes the yeast biosynthetic regulome
M Mülleder, E Calvani, MT Alam, RK Wang, F Eckerstorfer, A Zelezniak, ...
Cell 167 (2), 553-565. e12, 2016
Metabolic network topology reveals transcriptional regulatory signatures of type 2 diabetes
A Zelezniak, TH Pers, S Soares, ME Patti, KR Patil
PLoS Comput Biol 6 (4), e1000729, 2010
Flux coupling and transcriptional regulation within the metabolic network of the photosynthetic bacterium Synechocystis sp. PCC6803
A Montagud, A Zelezniak, E Navarro, PF de Córdoba, JF Urchueguía, ...
Biotechnology journal 6 (3), 330-342, 2011
The metabolic background is a global player in Saccharomyces gene expression epistasis
MT Alam, A Zelezniak, M Mülleder, P Shliaha, R Schwarz, F Capuano, ...
Nature microbiology 1 (3), 1-10, 2016
Contribution of network connectivity in determining the relationship between gene expression and metabolite concentration changes
A Zelezniak, S Sheridan, KR Patil
PLoS Comput Biol 10 (4), e1003572, 2014
The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization
MT Alam, V Olin-Sandoval, A Stincone, MA Keller, A Zelezniak, BF Luisi, ...
Nature communications 8 (1), 1-13, 2017
Designing and interpreting ‘multi-omic’experiments that may change our understanding of biology
R Haas, A Zelezniak, J Iacovacci, S Kamrad, SJ Townsend, M Ralser
Current Opinion in Systems Biology 6, 37-45, 2017
Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition
J Vowinckel, A Zelezniak, R Bruderer, M Mülleder, L Reiter, M Ralser
Scientific reports 8 (1), 4346, 2018
Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours
T Yamada, AS Waller, J Raes, A Zelezniak, N Perchat, A Perret, ...
Molecular systems biology 8 (1), 581, 2012
Machine learning predicts the yeast metabolome from the quantitative proteome of kinase knockouts
A Zelezniak, J Vowinckel, F Capuano, CB Messner, V Demichev, ...
Cell systems 7 (3), 269-283. e6, 2018
Expanding functional protein sequence spaces using generative adversarial networks
D Repecka, V Jauniskis, L Karpus, E Rembeza, I Rokaitis, J Zrimec, ...
Nature Machine Intelligence, 1-10, 2021
Biochemical principles enabling metabolic cooperativity and phenotypic heterogeneity at the single cell level
K Campbell, L Herrera-Dominguez, C Correia-Melo, A Zelezniak, ...
Current Opinion in Systems Biology 8, 97-108, 2018
Ice-age climate adaptations trap the alpine marmot in a state of low genetic diversity
TI Gossmann, A Shanmugasundram, S Börno, L Duvaux, C Lemaire, ...
Current Biology 29 (10), 1712-1720. e7, 2019
ScanningSWATH enables ultra-fast proteomics using high-flow chromatography and minute-scale gradients
C Messner, V Demichev, N Bloomfield, G Ivosev, F Wasim, A Zelezniak, ...
bioRxiv, 656793, 2019
Clinical classifiers of COVID-19 infection from novel ultra-high-throughput proteomics
CB Messner, V Demichev, D Wendisch, L Michalick, M White, A Freiwald, ...
MedRxiv, 2020
Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure
J Zrimec, CS Börlin, F Buric, AS Muhammad, R Chen, V Siewers, ...
Nature communications 11 (1), 1-16, 2020
Performance of regression models as a function of experiment noise
G Li, J Zrimec, B Ji, J Geng, J Larsbrink, A Zelezniak, J Nielsen, ...
arXiv preprint arXiv:1912.08141, 2019
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