Noah Davidsohn

3.6k total citations · 1 hit paper
9 papers, 1.9k citations indexed

About

Noah Davidsohn is a scholar working on Molecular Biology, Pediatrics, Perinatology and Child Health and Genetics. According to data from OpenAlex, Noah Davidsohn has authored 9 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 2 papers in Pediatrics, Perinatology and Child Health and 2 papers in Genetics. Recurrent topics in Noah Davidsohn's work include CRISPR and Genetic Engineering (3 papers), Gene Regulatory Network Analysis (3 papers) and Microbial Metabolic Engineering and Bioproduction (3 papers). Noah Davidsohn is often cited by papers focused on CRISPR and Genetic Engineering (3 papers), Gene Regulatory Network Analysis (3 papers) and Microbial Metabolic Engineering and Bioproduction (3 papers). Noah Davidsohn collaborates with scholars based in United States, Denmark and United Kingdom. Noah Davidsohn's co-authors include George M. Church, Samira Kiani, Marcelle Tuttle, James J. Collins, Alejandro Chavez, Ron Weiss, Christopher D. Guzman, Benjamin E. Housden, Jonathan Scheiman and John Aach and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Methods.

In The Last Decade

Noah Davidsohn

8 papers receiving 1.9k citations

Hit Papers

Highly efficient Cas9-mediated transcriptional programming 2015 2026 2018 2022 2015 250 500 750 1000
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. Highly efficient Cas9-mediated transcriptional programming
    2015 1.2k citations Alejandro Chavez, Jonathan Scheiman et al. Nature Methods profile →

Peers

Noah Davidsohn
Benedikt Wefers Germany
Jonathan Y. Hsu United States
Anthony D’Ippolito United States
Kazuto Yoshimi Japan
Mika Kimura Japan
Ryan L. Collins United States
Andrew Gregg United States
Jiashun Zheng United States
Christopher M. Vockley United States
Benedikt Wefers Germany
Noah Davidsohn
Citations per year, relative to Noah Davidsohn Noah Davidsohn (= 1×) peers Benedikt Wefers

Countries citing papers authored by Noah Davidsohn

Since Specialization
Citations

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

Fields of papers citing papers by Noah Davidsohn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Noah Davidsohn

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

All Works

9 of 9 papers shown
1.
Chen, Yang, Rolf Adler, Hiroyuki Mori, et al.. (2025). Effects of FGF21, soluble TGFBR2, and environmental temperature on metabolic dysfunction in lipodystrophic mice. JCI Insight. 10(16).
2.
Hasan, Rokib, et al.. (2024). Gene Therapy-Mediated Partial Reprogramming Extends Lifespan and Reverses Age-Related Changes in Aged Mice. Cellular Reprogramming. 26(1). 24–32. 22 indexed citations
3.
Davidsohn, Noah, Matthew J. Pezone, Andyna Vernet, et al.. (2019). A single combination gene therapy treats multiple age-related diseases. Proceedings of the National Academy of Sciences. 116(47). 23505–23511. 70 indexed citations
4.
Zullo, Joseph, Derek Drake, Liviu Aron, et al.. (2019). Regulation of lifespan by neural excitation and REST. Nature. 574(7778). 359–364. 146 indexed citations
5.
Yeo, Nan Cher, Alejandro Chavez, Yingleong Chan, et al.. (2018). An enhanced CRISPR repressor for targeted mammalian gene regulation. Nature Methods. 15(8). 611–616. 323 indexed citations
6.
Chavez, Alejandro, Jonathan Scheiman, Suhani Vora, et al.. (2015). Highly efficient Cas9-mediated transcriptional programming. Nature Methods. 12(4). 326–328. 1183 indexed citations breakdown →
7.
Davidsohn, Noah, Jacob Beal, Samira Kiani, et al.. (2014). Accurate Predictions of Genetic Circuit Behavior from Part Characterization and Modular Composition. ACS Synthetic Biology. 4(6). 673–681. 53 indexed citations
8.
Beal, Jacob, Ron Weiss, Fusun Yaman, Noah Davidsohn, & Aaron Adler. (2012). A Method for Fast, High-Precision Characterization of Synthetic Biology Devices. DSpace@MIT (Massachusetts Institute of Technology). 15 indexed citations
9.
Beal, Jacob, Ron Weiss, Douglas Densmore, et al.. (2012). An End-to-End Workflow for Engineering of Biological Networks from High-Level Specifications. ACS Synthetic Biology. 1(8). 317–331. 71 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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