David H. Perlman

2.5k total citations · 1 hit paper
20 papers, 1.4k citations indexed

About

David H. Perlman is a scholar working on Molecular Biology, Spectroscopy and Animal Science and Zoology. According to data from OpenAlex, David H. Perlman has authored 20 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 13 papers in Spectroscopy and 2 papers in Animal Science and Zoology. Recurrent topics in David H. Perlman's work include Advanced Proteomics Techniques and Applications (13 papers), Mass Spectrometry Techniques and Applications (8 papers) and Single-cell and spatial transcriptomics (4 papers). David H. Perlman is often cited by papers focused on Advanced Proteomics Techniques and Applications (13 papers), Mass Spectrometry Techniques and Applications (8 papers) and Single-cell and spatial transcriptomics (4 papers). David H. Perlman collaborates with scholars based in United States, United Kingdom and Bangladesh. David H. Perlman's co-authors include Catherine E. Costello, Mark E. McComb, Nikolai Slavov, R. Gray Huffman, Harrison Specht, Aleksandra A. Petelski, Vivek Bhatia, Emily H Emmott, Jianming Hu and Tom W. Muir and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Annual Review of Biochemistry.

In The Last Decade

David H. Perlman

20 papers receiving 1.4k citations

Hit Papers

Single-cell proteomic and transcriptomic analysis of macr... 2021 2026 2022 2024 2021 100 200 300

Peers

David H. Perlman
Henri Sasmor United States
Matthew Fitzgibbon United States
Laura F. Steel United States
Wei Mi China
Salvatore Sechi United States
Ricardo Núñez Miguel United Kingdom
Keli Ou Australia
David H. Perlman
Citations per year, relative to David H. Perlman David H. Perlman (= 1×) peers Morten Beck Trelle

Countries citing papers authored by David H. Perlman

Since Specialization
Citations

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

Fields of papers citing papers by David H. Perlman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David H. Perlman. 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 David H. Perlman. The network helps show where David H. Perlman may publish in the future.

Co-authorship network of co-authors of David H. Perlman

This figure shows the co-authorship network connecting the top 25 collaborators of David H. Perlman. A scholar is included among the top collaborators of David H. Perlman 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 David H. Perlman. David H. Perlman 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.
Huffman, R. Gray, Andrew Leduc, Christoph Wichmann, et al.. (2023). Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics. Nature Methods. 20(5). 714–722. 56 indexed citations
2.
Specht, Harrison, Emily H Emmott, Aleksandra A. Petelski, et al.. (2021). Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome biology. 22(1). 50–50. 325 indexed citations breakdown →
3.
Petelski, Aleksandra A., Emily H Emmott, Andrew Leduc, et al.. (2021). Multiplexed single-cell proteomics using SCoPE2. Nature Protocols. 16(12). 5398–5425. 144 indexed citations
4.
Emmott, Emily H, R. Gray Huffman, Peter V. Kharchenko, et al.. (2020). How to perform quantitative single cell proteomics with SCoPE2. Journal of Biomolecular Techniques JBT. 31. 1 indexed citations
5.
Perlman, David H., et al.. (2016). Digestomics: an emerging strategy for comprehensive analysis of protein catabolism. Current Opinion in Biotechnology. 43. 134–140. 8 indexed citations
6.
Oslund, Rob, Jung‐Min Kee, Anthony D. Couvillon, et al.. (2014). A Phosphohistidine Proteomics Strategy Based on Elucidation of a Unique Gas-Phase Phosphopeptide Fragmentation Mechanism. Journal of the American Chemical Society. 136(37). 12899–12911. 65 indexed citations
7.
Kee, Jung‐Min, Rob Oslund, David H. Perlman, & Tom W. Muir. (2013). A pan-specific antibody for direct detection of protein histidine phosphorylation. Nature Chemical Biology. 9(7). 416–421. 122 indexed citations
8.
Zhao, Xin, et al.. (2012). Regulation of Yeast Pyruvate Kinase by Ultrasensitive Allostery Independent of Phosphorylation. Molecular Cell. 48(1). 52–62. 60 indexed citations
9.
Khan, Zia, Sasan Amini, J. S. Bloom, et al.. (2011). Accurate proteome-wide protein quantification from high-resolution 15N mass spectra. Genome biology. 12(12). R122–R122. 15 indexed citations
10.
Ying, Wantao, David H. Perlman, Lei Li, et al.. (2009). Highly efficient and selective enrichment of peptide subsets combining fluorous chemistry with reversed‐phase chromatography. Rapid Communications in Mass Spectrometry. 23(24). 4019–4030. 16 indexed citations
11.
Romesser, Paul B., David H. Perlman, Douglas V. Faller, et al.. (2009). Development of a Malignancy-Associated Proteomic Signature for Diffuse Large B-Cell Lymphoma. American Journal Of Pathology. 175(1). 25–35. 12 indexed citations
12.
Bhatia, Vivek, David H. Perlman, Catherine E. Costello, & Mark E. McComb. (2009). Software Tool for Researching Annotations of Proteins: Open-Source Protein Annotation Software with Data Visualization. Analytical Chemistry. 81(23). 9819–9823. 193 indexed citations
13.
Lavatelli, Francesca, David H. Perlman, Brian Spencer, et al.. (2008). Amyloidogenic and Associated Proteins in Systemic Amyloidosis Proteome of Adipose Tissue. Molecular & Cellular Proteomics. 7(8). 1570–1583. 115 indexed citations
14.
Perlman, David H., Hua Huang, Claire Dauly, Catherine E. Costello, & Mark E. McComb. (2007). Coupling of Protein HPLC to MALDI-TOF MS Using an On-Target Device for Fraction Collection, Concentration, Digestion, Desalting, and Matrix/Analyte Cocrystallization. Analytical Chemistry. 79(5). 2058–2066. 10 indexed citations
16.
Basagoudanavar, Suresh H., David H. Perlman, & Jianming Hu. (2006). Regulation of Hepadnavirus Reverse Transcription by Dynamic Nucleocapsid Phosphorylation. Journal of Virology. 81(4). 1641–1649. 79 indexed citations
17.
Dauly, Claire, David H. Perlman, Catherine E. Costello, & Mark E. McComb. (2006). Protein Separation and Characterization by np-RP−HPLC Followed by Intact MALDI-TOF Mass Spectrometry and Peptide Mass Mapping Analyses. Journal of Proteome Research. 5(7). 1688–1700. 25 indexed citations
18.
Perlman, David H., Eric A. Berg, Peter B. O’Connor, Catherine E. Costello, & Jianming Hu. (2005). Reverse transcription-associated dephosphorylation of hepadnavirus nucleocapsids. Proceedings of the National Academy of Sciences. 102(25). 9020–9025. 99 indexed citations
19.
Gorenstein, P., et al.. (1979). Sealed Position Sensitive Hard X-Ray Detector Having Large Drift Region for All Sky Camera with High Angular Resolution. IEEE Transactions on Nuclear Science. 26(1). 502–505. 2 indexed citations
20.
Perlman, David H., et al.. (1971). Biosynthesis of Peptide Antibiotics. Annual Review of Biochemistry. 40(1). 449–464. 39 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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