Dariusz Plewczyński

46.7k total citations
163 papers, 2.9k citations indexed

About

Dariusz Plewczyński is a scholar working on Molecular Biology, Computational Theory and Mathematics and Genetics. According to data from OpenAlex, Dariusz Plewczyński has authored 163 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 123 papers in Molecular Biology, 23 papers in Computational Theory and Mathematics and 16 papers in Genetics. Recurrent topics in Dariusz Plewczyński's work include Genomics and Chromatin Dynamics (40 papers), Machine Learning in Bioinformatics (31 papers) and Protein Structure and Dynamics (28 papers). Dariusz Plewczyński is often cited by papers focused on Genomics and Chromatin Dynamics (40 papers), Machine Learning in Bioinformatics (31 papers) and Protein Structure and Dynamics (28 papers). Dariusz Plewczyński collaborates with scholars based in Poland, India and United States. Dariusz Plewczyński's co-authors include Krzysztof Ginalski, Michał Łaźniewski, Ziad Al Bkhetan, Leszek Rychlewski, Indrajit Saha, Rafał Augustyniak, Subhadip Basu, Ujjwal Maulik, Uwe Koch and Stéphane A. Spieser and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and The Journal of Chemical Physics.

In The Last Decade

Dariusz Plewczyński

157 papers receiving 2.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dariusz Plewczyński Poland 28 1.9k 602 208 187 177 163 2.9k
Bo Li China 33 2.3k 1.2× 333 0.6× 202 1.0× 266 1.4× 280 1.6× 198 4.3k
Jong Cheol Jeong United States 9 2.4k 1.2× 372 0.6× 257 1.2× 155 0.8× 94 0.5× 19 3.3k
Mark A. Moraes United States 6 1.8k 0.9× 827 1.4× 297 1.4× 116 0.6× 144 0.8× 7 3.3k
Ali Masoudi‐Nejad Iran 34 2.4k 1.2× 961 1.6× 222 1.1× 205 1.1× 407 2.3× 165 3.7k
Jinn‐Moon Yang Taiwan 32 2.3k 1.2× 894 1.5× 253 1.2× 244 1.3× 213 1.2× 157 4.3k
George M. Spyrou Greece 24 1.6k 0.8× 571 0.9× 86 0.4× 286 1.5× 65 0.4× 165 3.0k
Xing‐Ming Zhao China 38 3.5k 1.8× 677 1.1× 88 0.4× 304 1.6× 325 1.8× 183 4.9k
Hugo Ceulemans Belgium 30 3.0k 1.6× 789 1.3× 557 2.7× 235 1.3× 220 1.2× 59 4.4k
Jacob de Vlieg Netherlands 19 1.5k 0.8× 484 0.8× 140 0.7× 257 1.4× 158 0.9× 37 2.5k
Hans‐Peter Lenhof Germany 39 3.6k 1.9× 321 0.5× 159 0.8× 310 1.7× 208 1.2× 137 5.2k

Countries citing papers authored by Dariusz Plewczyński

Since Specialization
Citations

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

Fields of papers citing papers by Dariusz Plewczyński

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dariusz Plewczyński

This figure shows the co-authorship network connecting the top 25 collaborators of Dariusz Plewczyński. A scholar is included among the top collaborators of Dariusz Plewczyński 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 Dariusz Plewczyński. Dariusz Plewczyński 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.
Plewczyński, Dariusz, et al.. (2025). Improved cohesin HiChIP protocol and bioinformatic analysis for robust detection of chromatin loops and stripes. Communications Biology. 8(1). 437–437. 1 indexed citations
2.
3.
Dramiński, Michał, M Łapiński, Gabriela Adriana Filip, et al.. (2025). Unveiling Epigenetic Regulatory Elements Associated with Breast Cancer Development. International Journal of Molecular Sciences. 26(14). 6558–6558. 1 indexed citations
4.
Saha, Indrajit, Michał Łaźniewski, Ayatullah Faruk Mollah, et al.. (2024). Unveiling the Molecular Mechanism of Trastuzumab Resistance in SKBR3 and BT474 Cell Lines for HER2 Positive Breast Cancer. Current Issues in Molecular Biology. 46(3). 2713–2740. 11 indexed citations
5.
Plewczyński, Dariusz, et al.. (2024). Multiscale molecular modeling of chromatin with MultiMM: From nucleosomes to the whole genome. Computational and Structural Biotechnology Journal. 23. 3537–3548. 1 indexed citations
6.
Plewczyński, Dariusz, et al.. (2023). Prediction of chromatin looping using deep hybrid learning (DHL). Quantitative Biology. 11(2). 155–162. 1 indexed citations
7.
Tian, Simon Zhongyuan, Guoliang Li, Yang Yang, et al.. (2022). MCIBox: a toolkit for single-molecule multi-way chromatin interaction visualization and micro-domains identification. Briefings in Bioinformatics. 23(6). 5 indexed citations
8.
Chatterjee, Piyali, et al.. (2022). Rule-Based Pruning and In Silico Identification of Essential Proteins in Yeast PPIN. Cells. 11(17). 2648–2648. 2 indexed citations
9.
Grzywa, Tomasz M., Anna Sosnowska, Zuzanna Rydzyńska, et al.. (2021). Potent but transient immunosuppression of T-cells is a general feature of CD71+ erythroid cells. Communications Biology. 4(1). 1384–1384. 22 indexed citations
10.
Basu, Subhadip, et al.. (2020). The Mixture of Autoregressive Hidden Markov Models of Morphology for Dentritic Spines During Activation Process. Journal of Computational Biology. 27(9). 1471–1485. 3 indexed citations
11.
Trzaskoma, Paweł, Błażej Ruszczycki, Byoungkoo Lee, et al.. (2020). Ultrastructural visualization of 3D chromatin folding using volume electron microscopy and DNA in situ hybridization. Nature Communications. 11(1). 2120–2120. 28 indexed citations
12.
Chatterjee, Piyali, et al.. (2019). FunPred 3.0: improved protein function prediction using protein interaction network. PeerJ. 7. e6830–e6830. 11 indexed citations
13.
Stitzel, Michael L., et al.. (2016). Computational inference of H3K4me3 and H3K27ac domain length. PeerJ. 4. e1750–e1750. 6 indexed citations
14.
Szałaj, Przemysław, et al.. (2016). 3D-GNOME: an integrated web service for structural modeling of the 3D genome. Nucleic Acids Research. 44(W1). W288–W293. 30 indexed citations
15.
Plewczyński, Dariusz, et al.. (2016). Complexity curve: a graphical measure of data complexity and classifier performance. PeerJ Computer Science. 2. e76–e76. 10 indexed citations
16.
17.
Kazakiewicz, Denis, Jonathan R. Karr, Karol M. Langner, & Dariusz Plewczyński. (2015). A combined systems and structural modeling approach repositions antibiotics for Mycoplasma genitalium. Computational Biology and Chemistry. 59. 91–97. 8 indexed citations
18.
Basu, Subhadip, et al.. (2015). Multi-level machine learning prediction of protein–protein interactions in Saccharomyces cerevisiae. PeerJ. 3. e1041–e1041. 10 indexed citations
19.
Rączaszek‐Leonardi, Joanna, et al.. (2013). Information-Sharing in Three Interacting Minds Solving a Simple Perceptual Task. Cognitive Science. 35(35). 3 indexed citations
20.
Plewczyński, Dariusz, Subhadip Basu, & Indrajit Saha. (2012). AMS 4.0: consensus prediction of post-translational modifications in protein sequences. Amino Acids. 43(2). 573–582. 32 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Bo Li Breakdown of academic impact, for papers by Jong Cheol Jeong Breakdown of academic impact, for papers by Mark A. Moraes Breakdown of academic impact, for papers by Ali Masoudi‐Nejad Breakdown of academic impact, for papers by Jinn‐Moon Yang Breakdown of academic impact, for papers by George M. Spyrou Breakdown of academic impact, for papers by Xing‐Ming Zhao Breakdown of academic impact, for papers by Hugo Ceulemans Breakdown of academic impact, for papers by Jacob de Vlieg Breakdown of academic impact, for papers by Hans‐Peter Lenhof
Rankless by CCL
2026