Lukas M. Simon

9.0k total citations · 3 hit papers
47 papers, 3.4k citations indexed

About

Lukas M. Simon is a scholar working on Molecular Biology, Cancer Research and Hematology. According to data from OpenAlex, Lukas M. Simon has authored 47 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Molecular Biology, 11 papers in Cancer Research and 6 papers in Hematology. Recurrent topics in Lukas M. Simon's work include Single-cell and spatial transcriptomics (12 papers), Cancer-related molecular mechanisms research (9 papers) and Extracellular vesicles in disease (5 papers). Lukas M. Simon is often cited by papers focused on Single-cell and spatial transcriptomics (12 papers), Cancer-related molecular mechanisms research (9 papers) and Extracellular vesicles in disease (5 papers). Lukas M. Simon collaborates with scholars based in United States, Germany and United Kingdom. Lukas M. Simon's co-authors include Fabian J. Theis, Nikola S. Mueller, Maria Mircea, Gökçen Eraslan, Fiona Hamey, Joakim S. Dahlin, Jordi Solana, Berthold Göttgens, Nikolaus Rajewsky and Mireya Plass and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Circulation and Nature Medicine.

In The Last Decade

Lukas M. Simon

45 papers receiving 3.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells

2019 848 citations F. Alexander Wolf, Fiona Hamey et al. Genome biology profile →
Single-cell RNA-seq denoising using a deep count autoencoder

2019 611 citations Gökçen Eraslan, Lukas M. Simon et al. Nature Communications profile →
An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics

2019 390 citations Ilias Angelidis, Lukas M. Simon et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lukas M. Simon United States	18	2.3k	640	529	339	337	47	3.4k
Antonio Scialdone Italy	25	3.5k 1.5×	594 0.9×	546 1.0×	103 0.3×	360 1.1×	82	5.2k
Eli R. Zunder United States	16	3.0k 1.3×	356 0.6×	1.2k 2.3×	204 0.6×	630 1.9×	30	4.3k
Darci J. Phillips United States	20	1.4k 0.6×	310 0.5×	447 0.8×	122 0.4×	285 0.8×	31	2.4k
Wouter Saelens Belgium	14	2.8k 1.2×	589 0.9×	1.5k 2.8×	297 0.9×	423 1.3×	20	4.2k
Florian Buettner Germany	26	3.6k 1.6×	697 1.1×	855 1.6×	385 1.1×	649 1.9×	52	5.3k
Michael Khan United Kingdom	24	2.0k 0.9×	493 0.8×	258 0.5×	168 0.5×	137 0.4×	62	3.4k
Roser Vento‐Tormo United Kingdom	20	3.1k 1.4×	792 1.2×	1.7k 3.2×	271 0.8×	343 1.0×	35	4.6k
Jeroen A.M. Beliën Netherlands	34	1.7k 0.7×	854 1.3×	381 0.7×	415 1.2×	133 0.4×	88	4.2k
Joshua Gould United States	12	2.7k 1.2×	810 1.3×	601 1.1×	468 1.4×	243 0.7×	17	4.1k

Countries citing papers authored by Lukas M. Simon

Since Specialization

Citations

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

Fields of papers citing papers by Lukas M. Simon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lukas M. Simon. 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 Lukas M. Simon. The network helps show where Lukas M. Simon may publish in the future.

Co-authorship network of co-authors of Lukas M. Simon

This figure shows the co-authorship network connecting the top 25 collaborators of Lukas M. Simon. A scholar is included among the top collaborators of Lukas M. Simon 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 Lukas M. Simon. Lukas M. Simon 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

Liu, Andi, Xian Chen, Astrid M. Manuel, et al.. (2025). Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer’s disease. Science Advances. 11(51). eadw4917–eadw4917.

Henke, David, Alexander Renwick, Jitendra K. Meena, et al.. (2025). Bio-primed machine learning to enhance discovery of relevant biomarkers. npj Precision Oncology. 9(1). 39–39. 4 indexed citations

Yan, Fangfang, Akiko Suzuki, Guangsheng Pei, et al.. (2024). Single-cell multiomics decodes regulatory programs for mouse secondary palate development. Nature Communications. 15(1). 821–821. 8 indexed citations

Itai, Toshiyuki, Andi Liu, Sarah W. Curtis, et al.. (2024). Investigating gene functions and single-cell expression profiles of de novo variants in orofacial clefts. Human Genetics and Genomics Advances. 5(3). 100313–100313.

Dai, Yulin, Toshiyuki Itai, Guangsheng Pei, et al.. (2024). DeepFace: Deep-learning-based framework to contextualize orofacial-cleft-related variants during human embryonic craniofacial development. Human Genetics and Genomics Advances. 5(3). 100312–100312. 4 indexed citations

Wang, Mingqiang, Wankun Deng, David C. Samuels, Zhongming Zhao, & Lukas M. Simon. (2023). MitoTrace: A Computational Framework for Analyzing Mitochondrial Variation in Single-Cell RNA Sequencing Data. Genes. 14(6). 1222–1222. 2 indexed citations

Simon, Lukas M., Cecilia G. Flocco, David Henke, et al.. (2023). Microbial fingerprints reveal interaction between museum objects, curators, and visitors. iScience. 26(9). 107578–107578. 4 indexed citations

Yan, Fangfang, Zhongming Zhao, & Lukas M. Simon. (2021). EmptyNN: A neural network based on positive and unlabeled learning to remove cell-free droplets and recover lost cells in scRNA-seq data. Patterns. 2(8). 100311–100311. 10 indexed citations

Simon, Lukas M., Fangfang Yan, & Zhongming Zhao. (2020). DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data. GigaScience. 9(12). 7 indexed citations

10.

Pei, Guangsheng, Yin‐Ying Wang, Lukas M. Simon, et al.. (2020). Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders. Genome Research. 31(1). 146–158. 9 indexed citations

11.

Schiller, Herbert B., Daniel T. Montoro, Lukas M. Simon, et al.. (2019). The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease. American Journal of Respiratory Cell and Molecular Biology. 61(1). 31–41. 132 indexed citations

12.

Angelidis, Ilias, Lukas M. Simon, Isis E. Fernandez, et al.. (2019). An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics. Nature Communications. 10(1). 963–963. 390 indexed citations breakdown →

13.

Eraslan, Gökçen, Lukas M. Simon, Maria Mircea, Nikola S. Mueller, & Fabian J. Theis. (2019). Single-cell RNA-seq denoising using a deep count autoencoder. Nature Communications. 10(1). 390–390. 611 indexed citations breakdown →

14.

Wolf, F. Alexander, Fiona Hamey, Mireya Plass, et al.. (2019). PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome biology. 20(1). 59–59. 848 indexed citations breakdown →

15.

Simon, Lukas M., Alexander J. Westermann, Marion Engel, et al.. (2018). MetaMap: an atlas of metatranscriptomic reads in human disease-related RNA-seq data. GigaScience. 7(6). 16 indexed citations

16.

Tricoci, Pierluigi, Megan L. Neely, Leonard C. Edelstein, et al.. (2018). Effects of genetic variation in protease activated receptor 4 after an acute coronary syndrome: Analysis from the TRACER trial. Blood Cells Molecules and Diseases. 72. 37–43. 7 indexed citations

17.

Henke, David, Andrew R. Ghazi, Marvin T. Nieman, et al.. (2018). The protease‐activated receptor 4 Ala120Thr variant alters platelet responsiveness to low‐dose thrombin and protease‐activated receptor 4 desensitization, and is blocked by non‐competitive P2Y12 inhibition. Journal of Thrombosis and Haemostasis. 16(12). 2501–2514. 20 indexed citations

18.

Zou, Siying, Myrto Kostadima, William J. Astle, et al.. (2017). SNP in human ARHGEF3 promoter is associated with DNase hypersensitivity, transcript level and platelet function, and Arhgef3 KO mice have increased mean platelet volume. PLoS ONE. 12(5). e0178095–e0178095. 13 indexed citations

19.

Kong, Xianguo, et al.. (2017). Identification of a functional genetic variant driving racially dimorphic platelet gene expression of the thrombin receptor regulator, PCTP. Thrombosis and Haemostasis. 117(5). 962–970. 4 indexed citations

20.

Edelstein, Leonard C., Lukas M. Simon, Michael Holinstat, et al.. (2013). Racial differences in human platelet PAR4 reactivity reflect expression of PCTP and miR-376c. Nature Medicine. 19(12). 1609–1616. 161 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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