Dezső Ribli

2.3k total citations
11 papers, 641 citations indexed

About

Dezső Ribli is a scholar working on Molecular Biology, Astronomy and Astrophysics and Artificial Intelligence. According to data from OpenAlex, Dezső Ribli has authored 11 papers receiving a total of 641 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 4 papers in Astronomy and Astrophysics and 4 papers in Artificial Intelligence. Recurrent topics in Dezső Ribli's work include Galaxies: Formation, Evolution, Phenomena (4 papers), Cancer Genomics and Diagnostics (3 papers) and Gaussian Processes and Bayesian Inference (3 papers). Dezső Ribli is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (4 papers), Cancer Genomics and Diagnostics (3 papers) and Gaussian Processes and Bayesian Inference (3 papers). Dezső Ribli collaborates with scholars based in Hungary, United States and Denmark. Dezső Ribli's co-authors include István Csabai, Zoltán Szállási, Bálint Pataki, Marcin Krzystanek, Orsolya Pipek, Ádám Póti, Dávid Szüts, Bernadett Szikriszt, Charles Swanton and Sándor Spisák and has published in prestigious journals such as Cell, Oncogene and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

Dezső Ribli

10 papers receiving 632 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dezső Ribli Hungary	8	347	225	172	119	98	11	641
Michael Rose Germany	21	515 1.5×	191 0.8×	108 0.6×	276 2.3×	49 0.5×	51	1.0k
Anna D’Angelo Italy	18	421 1.2×	138 0.6×	88 0.5×	110 0.9×	189 1.9×	59	968
Pedro G. Ferreira Portugal	17	755 2.2×	178 0.8×	26 0.2×	42 0.4×	260 2.7×	44	1.2k
Paola Lecca Italy	14	381 1.1×	81 0.4×	100 0.6×	56 0.5×	57 0.6×	77	737
Chunfeng Zhang China	18	703 2.0×	279 1.2×	89 0.5×	182 1.5×	24 0.2×	36	1.0k
D. Gulhan United States	12	452 1.3×	288 1.3×	89 0.5×	301 2.5×	133 1.4×	30	794
Mark Robertson‐Tessi United States	21	588 1.7×	464 2.1×	108 0.6×	452 3.8×	160 1.6×	35	1.3k
Chit Hong Yam United States	11	319 0.9×	57 0.3×	48 0.3×	179 1.5×	31 0.3×	27	780
Alex Graudenzi Italy	18	629 1.8×	229 1.0×	40 0.2×	69 0.6×	143 1.5×	67	900
P. Edén Sweden	8	79 0.2×	53 0.2×	40 0.2×	100 0.8×	125 1.3×	11	1.0k

Countries citing papers authored by Dezső Ribli

Since Specialization

Citations

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

Fields of papers citing papers by Dezső Ribli

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dezső Ribli

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

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

Pataki, Bálint, Dezső Ribli, Benedek Gyöngyösi, et al.. (2022). HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening. Scientific Data. 9(1). 370–370. 18 indexed citations

Ribli, Dezső, Bálint Pataki, José Manuel Zorrilla Matilla, et al.. (2019). Weak lensing cosmology with convolutional neural networks on noisy data. Monthly Notices of the Royal Astronomical Society. 490(2). 1843–1860. 69 indexed citations

Ribli, Dezső, László Dobos, & István Csabai. (2019). Galaxy shape measurement with convolutional neural networks. Monthly Notices of the Royal Astronomical Society. 489(4). 4847–4859. 6 indexed citations

Németh, Eszter, Marcin Krzystanek, Lilla Reiniger, et al.. (2019). The genomic imprint of cancer therapies helps timing the formation of metastases. International Journal of Cancer. 145(3). 694–704. 1 indexed citations

Ribli, Dezső, Bálint Pataki, & István Csabai. (2018). Learning from deep learning: better cosmological parameter inference from weak lensing maps. arXiv (Cornell University).

Takeda, David Y., Sándor Spisák, Ji-Heui Seo, et al.. (2018). A Somatically Acquired Enhancer of the Androgen Receptor Is a Noncoding Driver in Advanced Prostate Cancer. Cell. 174(2). 422–432.e13. 202 indexed citations

Ribli, Dezső, Bálint Pataki, & István Csabai. (2018). An improved cosmological parameter inference scheme motivated by deep learning. Nature Astronomy. 3(1). 93–98. 39 indexed citations

Pipek, Orsolya, Dezső Ribli, J. Molnár, et al.. (2017). Fast and accurate mutation detection in whole genome sequences of multiple isogenic samples with IsoMut. BMC Bioinformatics. 18(1). 73–73. 22 indexed citations

Szikriszt, Bernadett, Ádám Póti, Orsolya Pipek, et al.. (2016). A comprehensive survey of the mutagenic impact of common cancer cytotoxics. Genome biology. 17(1). 99–99. 128 indexed citations

10.

Zámborszky, Judit, Bernadett Szikriszt, Orsolya Pipek, et al.. (2016). Loss of BRCA1 or BRCA2 markedly increases the rate of base substitution mutagenesis and has distinct effects on genomic deletions. Oncogene. 36(6). 746–755. 98 indexed citations

11.

Galamb, Orsolya, Alexandra Kalmár, Bálint Péterfia, et al.. (2016). Aberrant DNA methylation of WNT pathway genes in the development and progression of CIMP-negative colorectal cancer. Epigenetics. 11(8). 588–602. 58 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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