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2020 – today
- 2023
[j7]Andrei Dmitrenko, Michelle Reid, Nicola Zamboni
:
Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data. Bioinform. 39(3) (2023)- 2022
- [c3]Andrei Dmitrenko, Mauro M. Masiero, Nicola Zamboni:
Comparing representations of biological data learned with different AI paradigms, augmenting and cropping strategies. MIDL 2022: 353-369 - [c2]Andrei Dmitrenko, Mauro M. Masiero, Nicola Zamboni:
Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging data. MIDL 2022: 370-386 - [i4]Andrei Dmitrenko, Mauro M. Masiero, Nicola Zamboni:
Comparing representations of biological data learned with different AI paradigms, augmenting and cropping strategies. CoRR abs/2203.04107 (2022) - [i3]Andrei Dmitrenko, Mauro M. Masiero, Nicola Zamboni:
Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging data. CoRR abs/2203.04289 (2022) - [i2]Corey Broeckling, Timothy M. D. Ebbels, Ewy A. Mathé, Nicola Zamboni, Cecilia Wieder:
Computational Metabolomics: From Spectra to Knowledge (Dagstuhl Seminar 22181). Dagstuhl Reports 12(5): 1-16 (2022) - 2020
- [c1]Adrian Egli, Manuel Battegay, Andrea C. Büchler, Peter Bühlmann, Thierry Calandra, Philippe Eckert, Hansjakob Furrer, Gilbert Greub, Stephan M. Jakob, Laurent Kaiser, Stephen L. Leib, Stephan Marsch, Nicolai Meinshausen, Jean-Luc Pagani, Jerome Pugin, Gunnar Rätsch, Jacques Schrenzel, Reto Schüpbach, Martin Siegemund, Nicola Zamboni, Reinhard Zbinden, Annelies Zinkernagel, Karsten M. Borgwardt:
SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research. MIE 2020: 1163-1167 - [i1]Sebastian Böcker, Corey Broeckling, Emma Schymanski, Nicola Zamboni:
Computational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051). Dagstuhl Reports 10(1): 144-159 (2020)
2010 – 2019
- 2017
- [j6]Andreas Kühne, Urs Mayr, Daniel C. Sévin, Manfred Claassen, Nicola Zamboni:
Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data. PLoS Comput. Biol. 13(6) (2017) - 2016
- [j5]Maria Kogadeeva, Nicola Zamboni:
SUMOFLUX: A Generalized Method for Targeted 13C Metabolic Flux Ratio Analysis. PLoS Comput. Biol. 12(9) (2016) - 2012
- [j4]Markus Heinonen, Huibin Shen, Nicola Zamboni, Juho Rousu:
Metabolite identification and molecular fingerprint prediction through machine learning. Bioinform. 28(18): 2333-2341 (2012)
2000 – 2009
- 2008
- [j3]Ari Rantanen, Juho Rousu, Paula Jouhten, Nicola Zamboni, Hannu Maaheimo, Esko Ukkonen:
An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments. BMC Bioinform. 9 (2008) - [j2]Nicola Zamboni, Anne Kümmel, Matthias Heinemann:
anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data. BMC Bioinform. 9 (2008) - 2005
- [j1]Nicola Zamboni, Eliane Fischer, Uwe Sauer:
FiatFlux - a software for metabolic flux analysis from 13C-glucose experiments. BMC Bioinform. 6: 209 (2005)
Coauthor Index
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