Dmitry Svetlichnyy

2.5k total citations · 3 hit papers
7 papers, 1.6k citations indexed

About

Dmitry Svetlichnyy is a scholar working on Molecular Biology, Cardiology and Cardiovascular Medicine and Oncology. According to data from OpenAlex, Dmitry Svetlichnyy has authored 7 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 1 paper in Cardiology and Cardiovascular Medicine and 1 paper in Oncology. Recurrent topics in Dmitry Svetlichnyy's work include Genomics and Chromatin Dynamics (4 papers), RNA Research and Splicing (2 papers) and Gene expression and cancer classification (1 paper). Dmitry Svetlichnyy is often cited by papers focused on Genomics and Chromatin Dynamics (4 papers), RNA Research and Splicing (2 papers) and Gene expression and cancer classification (1 paper). Dmitry Svetlichnyy collaborates with scholars based in Belgium, United States and Israel. Dmitry Svetlichnyy's co-authors include Stein Aerts, Zeynep Kalender Atak, Hana Imrichová, Mark Fiers, Gert Hulselmans, Valerie Christiaens, Annelien Verfaillie, Jean‐Christophe Marine, Rekin’s Janky and Laura Standaert and has published in prestigious journals such as Cell, Nature Communications and Scientific Reports.

In The Last Decade

Dmitry Svetlichnyy

7 papers receiving 1.6k citations

Hit Papers

iRegulon: From a Gene Lis... 2014 2026 2018 2022 2014 2018 2020 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dmitry Svetlichnyy Belgium 7 908 383 295 217 138 7 1.6k
Kelly Street United States 11 1.5k 1.6× 589 1.5× 263 0.9× 329 1.5× 81 0.6× 15 2.3k
Gianni Monaco Italy 17 708 0.8× 489 1.3× 211 0.7× 144 0.7× 100 0.7× 44 1.6k
Michael D. Morgan United Kingdom 16 1.5k 1.7× 573 1.5× 195 0.7× 298 1.4× 125 0.9× 25 2.3k
Manuel Varas‐Godoy Chile 31 1.2k 1.3× 445 1.2× 170 0.6× 499 2.3× 94 0.7× 89 2.5k
Flavia Frabetti Italy 20 777 0.9× 252 0.7× 198 0.7× 127 0.6× 156 1.1× 53 1.6k
David Huss United States 24 1.0k 1.1× 534 1.4× 293 1.0× 125 0.6× 270 2.0× 45 2.2k
Timothy I. Shaw United States 19 1.2k 1.4× 285 0.7× 230 0.8× 142 0.7× 227 1.6× 58 2.0k
Avi Srivastava United States 14 1.8k 2.0× 559 1.5× 251 0.9× 428 2.0× 68 0.5× 24 2.7k
Qiao Li China 24 974 1.1× 429 1.1× 491 1.7× 473 2.2× 51 0.4× 100 2.0k
Nicolas Da Silva United States 30 1.6k 1.8× 473 1.2× 135 0.5× 249 1.1× 125 0.9× 37 2.8k

Countries citing papers authored by Dmitry Svetlichnyy

Since Specialization
Citations

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

Fields of papers citing papers by Dmitry Svetlichnyy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dmitry Svetlichnyy

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

All Works

7 of 7 papers shown
1.
Katzenelenbogen, Yonatan, Fadi Sheban, Adam Yalin, et al.. (2020). Coupled scRNA-Seq and Intracellular Protein Activity Reveal an Immunosuppressive Role of TREM2 in Cancer. Cell. 182(4). 872–885.e19. 309 indexed citations breakdown →
2.
Thorrez, Lieven, Dmitry Svetlichnyy, Liesbeth Zwarts, et al.. (2018). ACE-inhibition induces a cardioprotective transcriptional response in the metabolic syndrome heart. Scientific Reports. 8(1). 16169–16169. 356 indexed citations breakdown →
3.
Atak, Zeynep Kalender, Hana Imrichová, Dmitry Svetlichnyy, et al.. (2017). Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks. Genome Medicine. 9(1). 80–80. 11 indexed citations
4.
Verfaillie, Annelien, Dmitry Svetlichnyy, Hana Imrichová, et al.. (2016). Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic. Genome Research. 26(7). 882–895. 62 indexed citations
5.
Verfaillie, Annelien, Hana Imrichová, Zeynep Kalender Atak, et al.. (2015). Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state. Nature Communications. 6(1). 6683–6683. 280 indexed citations
6.
Svetlichnyy, Dmitry, Hana Imrichová, Mark Fiers, Zeynep Kalender Atak, & Stein Aerts. (2015). Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models. PLoS Computational Biology. 11(11). e1004590–e1004590. 17 indexed citations
7.
Janky, Rekin’s, Annelien Verfaillie, Hana Imrichová, et al.. (2014). iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections. PLoS Computational Biology. 10(7). e1003731–e1003731. 610 indexed citations breakdown →

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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