Ádám Halász

1.1k total citations
35 papers, 641 citations indexed

About

Ádám Halász is a scholar working on Molecular Biology, Computer Networks and Communications and Genetics. According to data from OpenAlex, Ádám Halász has authored 35 papers receiving a total of 641 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 7 papers in Computer Networks and Communications and 7 papers in Genetics. Recurrent topics in Ádám Halász's work include Gene Regulatory Network Analysis (13 papers), Distributed Control Multi-Agent Systems (7 papers) and Modular Robots and Swarm Intelligence (6 papers). Ádám Halász is often cited by papers focused on Gene Regulatory Network Analysis (13 papers), Distributed Control Multi-Agent Systems (7 papers) and Modular Robots and Swarm Intelligence (6 papers). Ádám Halász collaborates with scholars based in United States, France and Switzerland. Ádám Halász's co-authors include Vijay Kumar, Spring Berman, M. Ani Hsieh, Harvey Rubin, Jeremy S. Edwards, Marcin Imieliński, Călin Belta, George J. Pappas, A. Agung Julius and Krishnan Radhakrishnan and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Transactions on Automatic Control.

In The Last Decade

Ádám Halász

35 papers receiving 622 citations

Peers

Ádám Halász

CNC

GENET

Erik Rauch United States

Shuling Wang China

Fenghua He China

Martyn Amos United Kingdom

Yanmei Kang China

Nicolas Bredèche France

Andrew Wuensche United Kingdom

Nicholas S. Flann United States

Richard G. Morris Australia

Andrew Ilachinski United States

Erik Rauch United States

Ádám Halász

212 ×0.8

180 ×0.7

CNC

152 ×1.1

109 ×1.6

167 ×2.5

GENET

Citations per year, relative to Ádám Halász Ádám Halász (= 1×) peers Erik Rauch

Countries citing papers authored by Ádám Halász

Since Specialization

Citations

This map shows the geographic impact of Ádám Halász's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ádám Halász with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ádám Halász more than expected).

Fields of papers citing papers by Ádám Halász

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ádám Halász. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ádám Halász. The network helps show where Ádám Halász may publish in the future.

Co-authorship network of co-authors of Ádám Halász

This figure shows the co-authorship network connecting the top 25 collaborators of Ádám Halász. A scholar is included among the top collaborators of Ádám Halász based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ádám Halász. Ádám Halász is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Price, Bradley S., Maryam Khodaverdi, Brian Hendricks, et al.. (2024). Enhanced SARS-CoV-2 case prediction using public health data and machine learning models. JAMIA Open. 7(1). ooae014–ooae014. 1 indexed citations

Kerketta, Romica, et al.. (2022). Spatial Stochastic Model of the Pre-B Cell Receptor. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(1). 683–693. 1 indexed citations

Hendricks, Brian, Bradley S. Price, Wesley Kimble, et al.. (2022). If you build it, will they come? Is test site availability a root cause of geographic disparities in COVID-19 testing?. Public Health. 216. 21–26. 6 indexed citations

Güven, Emine, Michael J. Wester, Jeremy S. Edwards, & Ádám Halász. (2022). Modeling the Cluster Size Distribution of Vascular Endothelial Growth Factor (VEGF) Receptors. Bioinformatics and Biology Insights. 16. 749650326–749650326. 1 indexed citations

Ding, Xun, Lucas Carter, Priyanka Singh, et al.. (2021). Highly Accurate Chip-Based Resequencing of SARS-CoV-2 Clinical Samples. Langmuir. 37(16). 4763–4771. 6 indexed citations

Price, Bradley S., Maryam Khodaverdi, Ádám Halász, et al.. (2021). Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach. PLoS ONE. 16(11). e0259538–e0259538. 6 indexed citations

Kerketta, Romica, Ádám Halász, Mara P. Steinkamp, Bridget S. Wilson, & Jeremy S. Edwards. (2016). Effect of Spatial Inhomogeneities on the Membrane Surface on Receptor Dimerization and Signal Initiation. Frontiers in Cell and Developmental Biology. 4. 81–81. 3 indexed citations

Steinkamp, Mara P., Ádám Halász, Ye Chen, et al.. (2015). Orchestration of ErbB3 signaling through heterointeractions and homointeractions. Molecular Biology of the Cell. 26(22). 4109–4123. 13 indexed citations

Low-Nam, Shalini T., et al.. (2013). Dynamic Transition States of ErbB1 Phosphorylation Predicted by Spatial Stochastic Modeling. Biophysical Journal. 105(6). 1533–1543. 7 indexed citations

10.

Halász, Ádám, et al.. (2013). Analytical Solution of Steady-State Equations for Chemical Reaction Networks with Bilinear Rate Laws. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 10(4). 957–969. 7 indexed citations

11.

Chen‐Izu, Ye, et al.. (2013). The impact of high density receptor clusters on VEGF signaling. SHILAP Revista de lepidopterología. 125. 37–52. 6 indexed citations

12.

Radhakrishnan, Krishnan, et al.. (2012). Mathematical Simulation of Membrane Protein Clustering for Efficient Signal Transduction. Annals of Biomedical Engineering. 40(11). 2307–2318. 21 indexed citations

13.

Berman, Spring, Radhika Nagpal, & Ádám Halász. (2011). Optimization of stochastic strategies for spatially inhomogeneous robot swarms: A case study in commercial pollination. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. 1 indexed citations

14.

Radhakrishnan, Krishnan, Ádám Halász, Dionisios G. Vlachos, & Jeremy S. Edwards. (2010). Quantitative understanding of cell signaling: the importance of membrane organization. Current Opinion in Biotechnology. 21(5). 677–682. 32 indexed citations

15.

Hsieh, M. Ani, et al.. (2009). Specialization as an optimal strategy under varying external conditions. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1941–1946. 9 indexed citations

16.

Halász, Ádám, et al.. (2008). From discrete to continuous and back: Abstractions and mesoscopic phenomena in cells. 269–274. 1 indexed citations

17.

Berman, Spring, Ádám Halász, Vijay Kumar, & Stephen C. Pratt. (2007). Bio-Inspired Group Behaviors for the Deployment of a Swarm of Robots to Multiple Destinations. Proceedings - IEEE International Conference on Robotics and Automation/Proceedings. 38 indexed citations

18.

Imieliński, Marcin, Călin Belta, Harvey Rubin, & Ádám Halász. (2006). Systematic Analysis of Conservation Relations in Escherichia coli Genome-Scale Metabolic Network Reveals Novel Growth Media. Biophysical Journal. 90(8). 2659–2672. 31 indexed citations

19.

Halász, Ádám, et al.. (2006). Analysis of the Lactose metabolism in E. coli using sum-of-squares decomposition. 879–884. 4 indexed citations

20.

Imieliński, Marcin, Călin Belta, Ádám Halász, & Harvey Rubin. (2005). Investigating metabolite essentiality through genome-scale analysis of Escherichia coli production capabilities. Computer applications in the biosciences. 21(9). 2008–2016. 33 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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