Brian D. Aevermann

6.8k total citations
16 papers, 373 citations indexed

About

Brian D. Aevermann is a scholar working on Molecular Biology, Biophysics and Cancer Research. According to data from OpenAlex, Brian D. Aevermann has authored 16 papers receiving a total of 373 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 5 papers in Biophysics and 3 papers in Cancer Research. Recurrent topics in Brian D. Aevermann's work include Single-cell and spatial transcriptomics (9 papers), Cell Image Analysis Techniques (5 papers) and Extracellular vesicles in disease (3 papers). Brian D. Aevermann is often cited by papers focused on Single-cell and spatial transcriptomics (9 papers), Cell Image Analysis Techniques (5 papers) and Extracellular vesicles in disease (3 papers). Brian D. Aevermann collaborates with scholars based in United States, Australia and United Kingdom. Brian D. Aevermann's co-authors include Elizabeth R. Waters, Richard H. Scheuermann, Mark Novotny, Jeremy A. Miller, Ed S. Lein, Trygve E. Bakken, Yun Zhang, Rebecca D. Hodge, Roger S. Lasken and Boudewijn P. F. Lelieveldt and has published in prestigious journals such as Bioinformatics, PLoS ONE and PLANT PHYSIOLOGY.

In The Last Decade

Brian D. Aevermann

16 papers receiving 368 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Brian D. Aevermann United States 10 297 58 48 38 36 16 373
Shipeng Shao China 11 500 1.7× 61 1.1× 20 0.4× 16 0.4× 40 1.1× 32 609
Andrea Riba France 11 351 1.2× 136 2.3× 19 0.4× 77 2.0× 13 0.4× 13 586
Anthony J. Garrity United States 4 521 1.8× 45 0.8× 22 0.5× 22 0.6× 56 1.6× 5 567
Nikit Patel United States 7 419 1.4× 32 0.6× 10 0.2× 7 0.2× 16 0.4× 9 459
Yashar Sadian Germany 12 486 1.6× 29 0.5× 11 0.2× 10 0.3× 16 0.4× 14 550
Priya Sivaramakrishnan United States 9 694 2.3× 49 0.8× 60 1.3× 35 0.9× 39 1.1× 15 941
Christoph G. Gäbelein Switzerland 9 207 0.7× 63 1.1× 13 0.3× 23 0.6× 21 0.6× 13 348
Catriona Crombie United Kingdom 6 653 2.2× 65 1.1× 26 0.5× 38 1.0× 8 0.2× 8 797
Jonathan J. Turner United States 5 459 1.5× 102 1.8× 29 0.6× 16 0.4× 26 0.7× 6 560
Jan Küntzer Germany 7 239 0.8× 53 0.9× 36 0.8× 36 0.9× 7 0.2× 10 384

Countries citing papers authored by Brian D. Aevermann

Since Specialization
Citations

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

Fields of papers citing papers by Brian D. Aevermann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian D. Aevermann. 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 Brian D. Aevermann. The network helps show where Brian D. Aevermann may publish in the future.

Co-authorship network of co-authors of Brian D. Aevermann

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

All Works

16 of 16 papers shown
1.
Tan, Shawn Zheng Kai, Brian D. Aevermann, Tom Gillespie, et al.. (2023). Brain Data Standards - A method for building data-driven cell-type ontologies. Scientific Data. 10(1). 50–50. 7 indexed citations
2.
Miller, Jeremy A., Jeongbin Park, Boudewijn P. F. Lelieveldt, et al.. (2023). Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain. Scientific Reports. 13(1). 9567–9567. 9 indexed citations
3.
Zhang, Yun, Hao Sun, Brian D. Aevermann, et al.. (2022). FastMix: a versatile data integration pipeline for cell type-specific biomarker inference. Bioinformatics. 38(20). 4735–4744. 3 indexed citations
4.
Carrillo, Daniel, et al.. (2022). Machine learning for cell type classification from single nucleus RNA sequencing data. PLoS ONE. 17(9). e0275070–e0275070. 13 indexed citations
5.
Zhang, Yun, Brian D. Aevermann, Rohan Gala, & Richard H. Scheuermann. (2022). Cell type matching in single-cell RNA-sequencing data using FR-Match. Scientific Reports. 12(1). 9996–9996. 8 indexed citations
6.
Aevermann, Brian D., Yun Zhang, Mark Novotny, et al.. (2021). A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing. Genome Research. 31(10). 1767–1780. 38 indexed citations
7.
Aevermann, Brian D., Casey P. Shannon, Mark Novotny, et al.. (2021). Machine Learning-Based Single Cell and Integrative Analysis Reveals That Baseline mDC Predisposition Correlates With Hepatitis B Vaccine Antibody Response. Frontiers in Immunology. 12. 690470–690470. 10 indexed citations
8.
Abramson, Bradley W., Mark Novotny, Nolan T. Hartwick, et al.. (2021). The genome and preliminary single-nuclei transcriptome ofLemna minutareveals mechanisms of invasiveness. PLANT PHYSIOLOGY. 188(2). 879–897. 19 indexed citations
9.
Zhang, Yun, Brian D. Aevermann, Trygve E. Bakken, et al.. (2020). FR-Match: robust matching of cell type clusters from single cell RNA sequencing data using the Friedman–Rafsky non-parametric test. Briefings in Bioinformatics. 22(4). 9 indexed citations
10.
Aevermann, Brian D., Mark Novotny, Trygve E. Bakken, et al.. (2018). Cell type discovery using single-cell transcriptomics: implications for ontological representation. Human Molecular Genetics. 27(R1). R40–R47. 33 indexed citations
11.
Bakken, Trygve E., Lindsay G. Cowell, Brian D. Aevermann, et al.. (2017). Cell type discovery and representation in the era of high-content single cell phenotyping. BMC Bioinformatics. 18(S17). 559–559. 27 indexed citations
12.
Aevermann, Brian D., Jamison McCorrison, Pratap Venepally, et al.. (2016). PRODUCTION OF A PRELIMINARY QUALITY CONTROL PIPELINE FOR SINGLE NUCLEI RNA-SEQ AND ITS APPLICATION IN THE ANALYSIS OF CELL TYPE DIVERSITY OF POST-MORTEM HUMAN BRAIN NEOCORTEX. PubMed. 22. 564–575. 4 indexed citations
13.
Lee, Alexandra, Suman R. Das, Wei Wang, et al.. (2015). Diversifying Selection Analysis Predicts Antigenic Evolution of 2009 Pandemic H1N1 Influenza A Virus in Humans. Journal of Virology. 89(10). 5427–5440. 19 indexed citations
14.
Waters, Elizabeth R., et al.. (2008). Comparative analysis of the small heat shock proteins in three angiosperm genomes identifies new subfamilies and reveals diverse evolutionary patterns. Cell Stress and Chaperones. 13(2). 127–142. 137 indexed citations
15.
Aevermann, Brian D. & Elizabeth R. Waters. (2007). A comparative genomic analysis of the small heat shock proteins in Caenorhabditis elegans and briggsae. Genetica. 133(3). 307–319. 32 indexed citations
16.
Waters, Elizabeth R., et al.. (2005). Comparative analysis of the small heat shock proteins in three angiosperm genomes identifies new subfamilies and reveals diverse evolutionary patterns. Cell Stress and Chaperones. preprint(2007). 1–1. 5 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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