Dezső Módos

2.2k total citations
37 papers, 1.1k citations indexed

About

Dezső Módos is a scholar working on Molecular Biology, Computational Theory and Mathematics and Genetics. According to data from OpenAlex, Dezső Módos has authored 37 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 6 papers in Computational Theory and Mathematics and 5 papers in Genetics. Recurrent topics in Dezső Módos's work include Bioinformatics and Genomic Networks (14 papers), Computational Drug Discovery Methods (6 papers) and Gut microbiota and health (5 papers). Dezső Módos is often cited by papers focused on Bioinformatics and Genomic Networks (14 papers), Computational Drug Discovery Methods (6 papers) and Gut microbiota and health (5 papers). Dezső Módos collaborates with scholars based in United Kingdom, Hungary and Germany. Dezső Módos's co-authors include Tamás Korcsmáros, Dénes Türei, Katalin Lenti, Péter Csermely, Dávid Fazekas, John P. Thomas, Lejla Gul, Márton Ölbei, László Földvári-Nagy and Tibor Vellai and has published in prestigious journals such as Nature Communications, PLoS ONE and Scientific Reports.

In The Last Decade

Dezső Módos

35 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dezső Módos United Kingdom 15 747 126 124 119 103 37 1.1k
Carlos Alberto Vanegas Prieto Spain 20 865 1.2× 102 0.8× 71 0.6× 116 1.0× 51 0.5× 48 1.4k
Saikat Chakrabarti India 19 609 0.8× 94 0.7× 95 0.8× 186 1.6× 48 0.5× 57 1.1k
Tong Ying Shun United States 21 686 0.9× 139 1.1× 90 0.7× 106 0.9× 62 0.6× 35 1.4k
Anuradha Roy United States 18 656 0.9× 70 0.6× 79 0.6× 171 1.4× 112 1.1× 56 1.1k
Pablo Marín-García Spain 13 839 1.1× 69 0.5× 131 1.1× 157 1.3× 106 1.0× 24 1.4k
Hailong Zhu China 22 654 0.9× 121 1.0× 85 0.7× 154 1.3× 70 0.7× 62 1.2k
Alberto Pessia Finland 15 1.1k 1.5× 197 1.6× 165 1.3× 130 1.1× 104 1.0× 22 1.7k
Joshua A. Bittker United States 16 589 0.8× 140 1.1× 112 0.9× 127 1.1× 56 0.5× 31 909
Md. Zubbair Malik India 19 523 0.7× 152 1.2× 71 0.6× 95 0.8× 172 1.7× 89 928
Shaoxing Dai China 17 456 0.6× 67 0.5× 65 0.5× 91 0.8× 85 0.8× 56 1.0k

Countries citing papers authored by Dezső Módos

Since Specialization
Citations

This map shows the geographic impact of Dezső Módos'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 Dezső Módos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dezső Módos more than expected).

Fields of papers citing papers by Dezső Módos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dezső Módos. 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 Dezső Módos. The network helps show where Dezső Módos may publish in the future.

Co-authorship network of co-authors of Dezső Módos

This figure shows the co-authorship network connecting the top 25 collaborators of Dezső Módos. A scholar is included among the top collaborators of Dezső Módos 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 Dezső Módos. Dezső Módos is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Gul, Lejla, Márton Ölbei, Balázs Bohár, et al.. (2025). Protocol for predicting host-microbe interactions and their downstream effect on host cells using MicrobioLink. STAR Protocols. 6(1). 103570–103570.
2.
Mullish, Benjamin H., Márton Ölbei, Nathan Danckert, et al.. (2025). Deciphering the microbiome–metabolome landscape of an inflammatory bowel disease inception cohort. Gut Microbes. 17(1). 2527863–2527863. 2 indexed citations
3.
Bohár, Balázs, John P. Thomas, Yufan Liu, et al.. (2025). Patient-Specific Regulatory Network Rewiring in Inflammatory Bowel Disease: How Genetic Polymorphisms Divert Incoming Signals and Contribute to Disease Pathogenesis. Inflammatory Bowel Diseases. 31(10). 2665–2680.
4.
Módos, Dezső, Padhmanand Sudhakar, Matthew Madgwick, et al.. (2022). A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in ulcerative colitis. Nature Communications. 13(1). 2299–2299. 16 indexed citations
5.
Treveil, Agatha, Dezső Módos, Matthew Madgwick, et al.. (2022). Mapping the epithelial–immune cell interactome upon infection in the gut and the upper airways. npj Systems Biology and Applications. 8(1). 15–15. 4 indexed citations
6.
Braicu, Cornelia, Dezső Módos, Lajos Ráduly, et al.. (2022). Targeting Cell Death Mechanism Specifically in Triple Negative Breast Cancer Cell Lines. International Journal of Molecular Sciences. 23(9). 4784–4784. 3 indexed citations
7.
Thomas, John P., et al.. (2022). Analysing miRNA-Target Gene Networks in Inflammatory Bowel Disease and Other Complex Diseases Using Transcriptomic Data. Genes. 13(2). 370–370. 5 indexed citations
8.
Türei, Dénes, Alberto Valdeolivas, Lejla Gul, et al.. (2021). Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis. Molecular Systems Biology. 17(3). e9923–e9923. 164 indexed citations
9.
Treveil, Agatha, Balázs Bohár, Padhmanand Sudhakar, et al.. (2021). ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways. PLoS Computational Biology. 17(2). e1008685–e1008685. 7 indexed citations
10.
Módos, Dezső, John P. Thomas, & Tamás Korcsmáros. (2021). A handy meta-analysis tool for IBD research. Nature Computational Science. 1(9). 571–572. 4 indexed citations
11.
Ölbei, Márton, John P. Thomas, Isabelle Hautefort, et al.. (2021). CytokineLink: A Cytokine Communication Map to Analyse Immune Responses—Case Studies in Inflammatory Bowel Disease and COVID-19. Cells. 10(9). 2242–2242. 11 indexed citations
12.
Módos, Dezső, et al.. (2020). EMDIP: An Entropy Measure to Discover Important Proteins in PPI networks. Computers in Biology and Medicine. 120. 103740–103740. 6 indexed citations
13.
Módos, Dezső, et al.. (2020). Transcriptomics predicts compound synergy in drug and natural product treated glioblastoma cells. PLoS ONE. 15(9). e0239551–e0239551. 19 indexed citations
14.
Oerton, Erin, et al.. (2018). Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data. Molecular Omics. 14(4). 218–236. 74 indexed citations
15.
Módos, Dezső, Krishna C. Bulusu, Dávid Fazekas, et al.. (2017). Neighbours of cancer-related proteins have key influence on pathogenesis and could increase the drug target space for anticancer therapies. npj Systems Biology and Applications. 3(1). 2–2. 20 indexed citations
16.
Fazekas, Dávid, Dénes Türei, Balázs Horváth, et al.. (2016). SignaFish: A Zebrafish-Specific Signaling Pathway Resource. Zebrafish. 13(6). 541–544. 4 indexed citations
17.
Türei, Dénes, et al.. (2015). Targets of drugs are generally and targets of drugs having side effects are specifically good spreaders of human interactome perturbations. Scientific Reports. 5(1). 10182–10182. 21 indexed citations
18.
Türei, Dénes, László Földvári-Nagy, Dávid Fazekas, et al.. (2015). Autophagy Regulatory Network — A systems-level bioinformatics resource for studying the mechanism and regulation of autophagy. Autophagy. 11(1). 155–165. 74 indexed citations
19.
Türei, Dénes, Diána Papp, Dávid Fazekas, et al.. (2013). NRF2-ome: An Integrated Web Resource to Discover Protein Interaction and Regulatory Networks of NRF2. Oxidative Medicine and Cellular Longevity. 2013. 1–9. 39 indexed citations
20.
Módos, Dezső, et al.. (2013). Adaptation and learning of molecular networks as a description of cancer development at the systems-level: Potential use in anti-cancer therapies. Seminars in Cancer Biology. 23(4). 262–269. 27 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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