A. Gábor

发表

Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction

A. Beyer, J. Sáez-Rodríguez, Bernd Bodenmiller, 2020, bioRxiv.

Linear conjugacy in biochemical reaction networks with rational reaction rates

Gábor Szederkényi, Katalin M. Hangos, Attila Gábor, 2016, Journal of Mathematical Chemistry.

Reaction network realizations of rational biochemical systems and their structural properties

J. Banga, G. Szederkényi, K. Hangos, 2015, Journal of Mathematical Chemistry.

Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data

R. Krause, N. Rajewsky, P. Qiu, 2020, Life Science Alliance.

Explainable multi-view framework for dissecting intercellular signaling from highly multiplexed spatial data

Julio Saez-Rodriguez, Jovan Tanevski, Attila Gabor, 2020, bioRxiv.

Analysis of the Human Kinome and Phosphatome by Mass Cytometry Reveals Overexpression-Induced Effects on Cancer-Related Signaling

J. Saez-Rodriguez, Bernd Bodenmiller, C. von Mering, 2019, Molecular cell.

Analysis of the Human Kinome and Phosphatome Reveals Diseased Signaling Networks Induced by Overexpression

C. V. Mering, J. Saez-Rodriguez, Bernd Bodenmiller, 2018 .

Analysis of the human kinome and phosphatome reveals diseased signaling networks induced by overexpression

C. V. Mering, J. Saez-Rodriguez, Bernd Bodenmiller, 2018, bioRxiv.

Parallel ant colony optimization for the training of cell signaling networks

J. Banga, J. Sáez-Rodríguez, A. Gábor, 2022, Expert Syst. Appl..

Additional File 2 for Robust and Efficient Parameter Estimation in Dynamic Models of Biological Systems Methods for tuning regularization

J. Banga, A. Gábor, E. Cabello, 2015 .

Deciphering the signaling network of breast cancer improves drug sensitivity prediction.

A. Beyer, C. Caldas, O. Rueda, 2021, Cell systems.

Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data

Ke Xu, Thomas Yu, Julio Saez-Rodriguez, 2019, bioRxiv.

Robust and efficient parameter estimation in dynamic models of biological systems

J. Banga, A. Gábor, 2015, BMC Systems Biology.

Model analysis and parameter estimation in biochemical reaction networks

Attila Gábor, A. Gábor, 2017 .

Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems

Julio R. Banga, Alejandro Fernández Villaverde, Attila Gábor, 2017, BMC Systems Biology.

New insights into the mechanisms underlying 5-fluorouracil-induced intestinal toxicity based on transcriptomic and metabolomic responses in human intestinal organoids

J. Kleinjans, J. Saez-Rodriguez, Seung-Wook Chung, 2021, Archives of Toxicology.

Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis

Fabian J Theis, J. Saez-Rodriguez, T. Korcsmáros, 2021, Molecular systems biology.

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis

Tamás Korcsmáros, Attila Gábor, Dénes Türei, 2020, bioRxiv.

Explainable multiview framework for dissecting spatial relationships from highly multiplexed data

J. Sáez-Rodríguez, J. Tanevski, A. Gábor, 2022, Genome Biology.

Model reduction in bio-chemical reaction networks with Michaelis-Menten kinetics

Gábor Szederkényi, Katalin M. Hangos, Attila Gábor, 2013, 2013 European Control Conference (ECC).

AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology

Eva Balsa-Canto, David Henriques, Julio R. Banga, 2016, Bioinform..

Explainable multi-view framework for dissecting intercellular signaling from highly multiplexed spatial data

J. Saez-Rodriguez, J. Tanevski, A. Gábor, 2020, bioRxiv.

Final Comments by the Authors

Gábor Szederkényi, Katalin M. Hangos, Attila Gábor, 2010 .

Integration of temporal single cell cellular stress response activity with Logic-ODE modeling reveals activation of ATF4-CHOP axis as a critical predictor of drug-induced liver injury.

Sylvia E. Le Dévédec, J. Saez-Rodriguez, J. Hengstler, 2021, Biochemical pharmacology.

A Network-based Transcriptomic Landscape of HepG2 cells to Uncover Causal Gene Cytotoxicity Interactions Underlying Drug-Induced Liver Injury

Sylvia E. Le Dévédec, J. Saez-Rodriguez, B. van de Water, 2023, bioRxiv.

Decision modelling with HIPRE 3+

J. A. M. Wesseling, A. Gábor, A. Gábor, 1994 .

Bringing data from curated pathway resources to Cytoscape with OmniPath

Julio Saez-Rodriguez, Attila Gabor, Francesco Ceccarelli, 2019, Bioinform..

Modeling and identification of a nuclear reactor with temperature effects and Xenon poisoning

Gábor Szederkényi, Katalin M. Hangos, Attila Gábor, 2009, 2009 35th Annual Conference of IEEE Industrial Electronics.

Improved Parameter Estimation in Kinetic Models: Selection and Tuning of Regularization Methods

Julio R. Banga, Attila Gábor, J. Banga, 2014, CMSB.

On the Verification and Correction of Large-Scale Kinetic Models in Systems Biology

Julio R. Banga, Gábor Szederkényi, Katalin M. Hangos, 2013, CMSB.

Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results

J. Saez-Rodriguez, J. Tanevski, A. Gábor, 2023, bioRxiv.

Model complexity reduction of chemical reaction networks using mixed-integer quadratic programming

Gábor Szederkényi, Katalin M. Hangos, Attila Gábor, 2013, Comput. Math. Appl..

Robust and efficient parameter estimation in dynamic models of biological systems

Julio R. Banga, Attila Gábor, J. Banga, 2015, BMC Systems Biology.

A network-based transcriptomic landscape of HepG2 cells uncovering causal gene-cytotoxicity interactions underlying drug-induced liver injury

Sylvia E. Le Dévédec, A. Gábor, G. Callegaro, 2023, Toxicological sciences : an official journal of the Society of Toxicology.

Literature and data-driven based inference of signalling interactions using time-course data

Julio Saez-Rodriguez, Julio Saez-Rodriguez, Attila Gábor, 2019, IFAC-PapersOnLine.

Converting networks to predictive logic models from perturbation signalling data with CellNOpt

Alexander Mitsos, Aurelien Dugourd, Julio Saez-Rodriguez, 2020, bioRxiv.

Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd

J. Saez-Rodriguez, Thomas Yu, J. Tanevski, 2021, bioRxiv.