Nathan Lawlor

1.8k total citations · 1 hit paper
14 papers, 941 citations indexed

About

Nathan Lawlor is a scholar working on Molecular Biology, Surgery and Genetics. According to data from OpenAlex, Nathan Lawlor has authored 14 papers receiving a total of 941 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 6 papers in Surgery and 6 papers in Genetics. Recurrent topics in Nathan Lawlor's work include Pancreatic function and diabetes (6 papers), Epigenetics and DNA Methylation (5 papers) and Diabetes and associated disorders (4 papers). Nathan Lawlor is often cited by papers focused on Pancreatic function and diabetes (6 papers), Epigenetics and DNA Methylation (5 papers) and Diabetes and associated disorders (4 papers). Nathan Lawlor collaborates with scholars based in United States, Singapore and Türkiye. Nathan Lawlor's co-authors include Michael L. Stitzel, David S. Newburg, Yingying He, Romy Kursawe, Joshy George, Mohan Bolisetty, Paul Robson, Lili Sun, Ina Kycia and V. Sivakamasundari and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Nathan Lawlor

14 papers receiving 928 citations

Hit Papers

Single-cell transcriptomes identify human islet cell sign... 2016 2026 2019 2022 2016 100 200 300
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.

  1. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetes
    2016 345 citations Nathan Lawlor, Joshy George et al. Genome Research profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nathan Lawlor United States 11 452 307 271 266 152 14 941
JO Lindsay United Kingdom 9 454 1.0× 207 0.7× 217 0.8× 384 1.4× 166 1.1× 24 948
Yue Zheng China 14 412 0.9× 163 0.5× 75 0.3× 48 0.2× 83 0.5× 41 903
Richard Mauerer Germany 7 256 0.6× 258 0.8× 71 0.3× 132 0.5× 83 0.5× 11 742
Amika Singla United States 18 429 0.9× 226 0.7× 70 0.3× 75 0.3× 81 0.5× 29 811
Ivy Ka Man Law United States 16 472 1.0× 96 0.3× 78 0.3× 97 0.4× 235 1.5× 26 1.0k
Caroline Finlayson United Kingdom 16 186 0.4× 278 0.9× 87 0.3× 45 0.2× 158 1.0× 30 1.0k
Kazuya Takahama Japan 16 294 0.7× 216 0.7× 72 0.3× 214 0.8× 80 0.5× 38 683
Martine Glorian France 15 484 1.1× 118 0.4× 103 0.4× 69 0.3× 133 0.9× 21 1.1k
Mark A. Pilkinton United States 15 295 0.7× 65 0.2× 113 0.4× 77 0.3× 127 0.8× 23 835
Frédéric S. Michel South Africa 17 176 0.4× 163 0.5× 42 0.2× 115 0.4× 84 0.6× 45 746

Countries citing papers authored by Nathan Lawlor

Since Specialization
Citations

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

Fields of papers citing papers by Nathan Lawlor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nathan Lawlor

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

All Works

14 of 14 papers shown
1.
Rui, Jinxiu, Songyan Deng, Ana Luisa Perdigoto, et al.. (2021). Tet2 Controls the Responses of β cells to Inflammation in Autoimmune Diabetes. Nature Communications. 12(1). 5074–5074. 19 indexed citations
2.
Lawlor, Nathan, Djamel Nehar-Belaid, Jessica Grassmann, et al.. (2021). Single Cell Analysis of Blood Mononuclear Cells Stimulated Through Either LPS or Anti-CD3 and Anti-CD28. Frontiers in Immunology. 12. 636720–636720. 49 indexed citations
3.
Thibodeau, Asa, Alper Eroğlu, Christopher S. McGinnis, et al.. (2021). AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data. Genome biology. 22(1). 252–252. 35 indexed citations
4.
Lawlor, Nathan, Eladio J. Márquez, Donghyung Lee, & Duygu Ucar. (2020). V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data. Bioinformatics. 36(11). 3582–3584. 5 indexed citations
5.
Lawlor, Nathan & Michael L. Stitzel. (2019). (Epi)genomic heterogeneity of pancreatic islet function and failure in type 2 diabetes. Molecular Metabolism. 27. S15–S24. 8 indexed citations
6.
Youn, Ahrim, Eladio J. Márquez, Nathan Lawlor, Michael L. Stitzel, & Duygu Ucar. (2018). BiFET: sequencing Bi as-free transcription factor F ootprint E nrichment T est. Nucleic Acids Research. 47(2). e11–e11. 12 indexed citations
7.
Kursawe, Romy, Ahrim Youn, Nathan Lawlor, et al.. (2018). Type 2 Diabetes–Associated Genetic Variants Regulate Chromatin Accessibility in Human Islets. Diabetes. 67(11). 2466–2477. 40 indexed citations
8.
Lee, Donghyung, Anthony Cheng, Nathan Lawlor, Mohan Bolisetty, & Duygu Ucar. (2018). Detection of correlated hidden factors from single cell transcriptomes using Iteratively Adjusted-SVA (IA-SVA). Scientific Reports. 8(1). 17040–17040. 5 indexed citations
9.
Lawlor, Nathan, et al.. (2017). Genomics of Islet (Dys)function and Type 2 Diabetes. Trends in Genetics. 33(4). 244–255. 52 indexed citations
10.
Lawlor, Nathan, Ahrim Youn, Romy Kursawe, Duygu Ucar, & Michael L. Stitzel. (2017). Alpha TC1 and Beta-TC-6 genomic profiling uncovers both shared and distinct transcriptional regulatory features with their primary islet counterparts. Scientific Reports. 7(1). 11959–11959. 25 indexed citations
11.
Lawlor, Nathan, Joshy George, Mohan Bolisetty, et al.. (2016). Single-cell transcriptomes identify human islet cell signatures and reveal cell-type–specific expression changes in type 2 diabetes. Genome Research. 27(2). 208–222. 345 indexed citations breakdown →
12.
He, Yingying, Nathan Lawlor, & David S. Newburg. (2016). Human Milk Components Modulate Toll-Like Receptor–Mediated Inflammation. Advances in Nutrition. 7(1). 102–111. 117 indexed citations
13.
Lawlor, Nathan, et al.. (2016). multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles. Cancer Informatics. 15. CIN.S38000–CIN.S38000. 10 indexed citations
14.
He, Yingying, Shubai Liu, David E. Kling, et al.. (2014). The human milk oligosaccharide 2′-fucosyllactose modulates CD14 expression in human enterocytes, thereby attenuating LPS-induced inflammation. Gut. 65(1). 33–46. 219 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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