Adam L. MacLean

2.1k total citations
36 papers, 1.1k citations indexed

About

Adam L. MacLean is a scholar working on Molecular Biology, Oncology and Immunology. According to data from OpenAlex, Adam L. MacLean has authored 36 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 10 papers in Oncology and 9 papers in Immunology. Recurrent topics in Adam L. MacLean's work include Single-cell and spatial transcriptomics (15 papers), Gene Regulatory Network Analysis (9 papers) and Cancer Cells and Metastasis (9 papers). Adam L. MacLean is often cited by papers focused on Single-cell and spatial transcriptomics (15 papers), Gene Regulatory Network Analysis (9 papers) and Cancer Cells and Metastasis (9 papers). Adam L. MacLean collaborates with scholars based in United States, United Kingdom and Germany. Adam L. MacLean's co-authors include Qing Nie, Michael P. H. Stumpf, Shuxiong Wang, Guang Zhao, Suoqin Jin, Tao Peng, Tian Hong, Jinzhi Lei, Yucheng Guo and Yanda Li and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.

In The Last Decade

Adam L. MacLean

33 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
Adam L. MacLean United States 18 563 161 152 141 106 36 1.1k
Yongsoo Kim South Korea 24 771 1.4× 202 1.3× 150 1.0× 158 1.1× 30 0.3× 68 1.4k
Greg Schuler United States 4 1.3k 2.2× 180 1.1× 109 0.7× 127 0.9× 68 0.6× 4 1.7k
Johann M. Kraus Germany 21 734 1.3× 278 1.7× 97 0.6× 120 0.9× 14 0.1× 55 1.5k
Åsa Segerstolpe United States 11 1.6k 2.9× 240 1.5× 274 1.8× 104 0.7× 32 0.3× 20 2.5k
Sophia Liu United States 11 483 0.9× 164 1.0× 134 0.9× 63 0.4× 85 0.8× 28 928
Wasco Wruck Germany 21 1.2k 2.2× 185 1.1× 107 0.7× 88 0.6× 12 0.1× 83 1.9k
Byunghee Kang South Korea 9 964 1.7× 237 1.5× 254 1.7× 69 0.5× 18 0.2× 19 1.6k
Anja Füllgrabe United Kingdom 7 591 1.0× 74 0.5× 59 0.4× 92 0.7× 22 0.2× 7 807
Yong Yuan United States 18 779 1.4× 134 0.8× 101 0.7× 161 1.1× 24 0.2× 47 1.5k
Denis Torre United States 12 1.1k 1.9× 167 1.0× 210 1.4× 102 0.7× 9 0.1× 17 1.6k

Countries citing papers authored by Adam L. MacLean

Since Specialization
Citations

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

Fields of papers citing papers by Adam L. MacLean

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Adam L. MacLean. 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 Adam L. MacLean. The network helps show where Adam L. MacLean may publish in the future.

Co-authorship network of co-authors of Adam L. MacLean

This figure shows the co-authorship network connecting the top 25 collaborators of Adam L. MacLean. A scholar is included among the top collaborators of Adam L. MacLean 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 Adam L. MacLean. Adam L. MacLean 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.
2.
Chen, Yu-Lin, Jonathan O. Martinez, Norbert Geisler, et al.. (2025). Gene regulatory network inference with popInfer reveals the dynamic regulation of hematopoietic stem cell quiescence. iScience. 28(12). 114010–114010.
3.
Wu, Xiaojun, et al.. (2025). Data-driven model discovery and model selection for noisy biological systems. PLoS Computational Biology. 21(1). e1012762–e1012762. 3 indexed citations
4.
Lopez‐Burks, Martha E., et al.. (2024). Gastrulation-stage gene expression in Nipbl +/− mouse embryos foreshadows the development of syndromic birth defects. Science Advances. 10(12). eadl4239–eadl4239. 6 indexed citations
5.
Shibata, Darryl, et al.. (2024). Developmental hematopoietic stem cell variation explains clonal hematopoiesis later in life. Nature Communications. 15(1). 10268–10268. 2 indexed citations
6.
Torres, Evanthia T. Roussos, et al.. (2023). Myeloid-Derived Suppressor–Cell Dynamics Control Outcomes in the Metastatic Niche. Cancer Immunology Research. 11(5). 614–628. 9 indexed citations
7.
Xiong, Lingyun, Jing Liu, Kari Koppitch, et al.. (2023). Direct androgen receptor control of sexually dimorphic gene expression in the mammalian kidney. Developmental Cell. 58(21). 2338–2358.e5. 17 indexed citations
8.
MacLean, Adam L., et al.. (2021). A single-cell resolved cell-cell communication model explains lineage commitment in hematopoiesis. Development. 148(24). 5 indexed citations
9.
MacLean, Adam L., et al.. (2021). RVAgene: generative modeling of gene expression time series data. Bioinformatics. 37(19). 3252–3262. 12 indexed citations
10.
Yu, Min, et al.. (2021). Modeling the effects of EMT-immune dynamics on carcinoma disease progression. Communications Biology. 4(1). 983–983. 5 indexed citations
11.
Wang, Shuxiong, Michael L. Drummond, Christian F. Guerrero‐Juarez, et al.. (2020). Single cell transcriptomics of human epidermis identifies basal stem cell transition states. Nature Communications. 11(1). 4239–4239. 135 indexed citations
12.
Haensel, Daniel, Suoqin Jin, Peng Sun, et al.. (2020). Defining Epidermal Basal Cell States during Skin Homeostasis and Wound Healing Using Single-Cell Transcriptomics. Cell Reports. 30(11). 3932–3947.e6. 154 indexed citations
13.
Lambert, Ben, Adam L. MacLean, Alexander G. Fletcher, et al.. (2018). Bayesian inference of agent-based models: a tool for studying kidney branching morphogenesis. Journal of Mathematical Biology. 76(7). 1673–1697. 31 indexed citations
14.
Guo, Yucheng, Qing Nie, Adam L. MacLean, et al.. (2017). Multiscale Modeling of Inflammation-Induced Tumorigenesis Reveals Competing Oncogenic and Oncoprotective Roles for Inflammation. Cancer Research. 77(22). 6429–6441. 97 indexed citations
15.
Peng, Tao, Linan Liu, Adam L. MacLean, et al.. (2017). A mathematical model of mechanotransduction reveals how mechanical memory regulates mesenchymal stem cell fate decisions. BMC Systems Biology. 11(1). 55–55. 45 indexed citations
16.
MacLean, Adam L., Maia A. Smith, Juliane Liepe, et al.. (2017). Single Cell Phenotyping Reveals Heterogeneity Among Hematopoietic Stem Cells Following Infection. Stem Cells. 35(11). 2292–2304. 14 indexed citations
17.
Crowell, Helena L., Adam L. MacLean, & Michael P. H. Stumpf. (2016). Feedback mechanisms control coexistence in a stem cell model of acute myeloid leukaemia. Journal of Theoretical Biology. 401. 43–53. 17 indexed citations
18.
MacLean, Adam L., Paul Kirk, & Michael P. H. Stumpf. (2015). Cellular population dynamics control the robustness of the stem cell niche. Biology Open. 4(11). 1420–1426. 10 indexed citations
19.
MacLean, Adam L., Cristina Lo Celso, & Michael P. H. Stumpf. (2013). Population dynamics of normal and leukaemia stem cells in the haematopoietic stem cell niche show distinct regimes where leukaemia will be controlled. Journal of The Royal Society Interface. 10(81). 20120968–20120968. 22 indexed citations
20.
Zhao, Guang & Adam L. MacLean. (2000). A comparison of canonical discriminant analysis and principal component analysis for spectral transformation.. Photogrammetric Engineering & Remote Sensing. 66(7). 841–847. 63 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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