Michelle DeWitt

1.3k total citations · 1 hit paper
7 papers, 318 citations indexed

About

Michelle DeWitt is a scholar working on Molecular Biology, General Health Professions and Surgery. According to data from OpenAlex, Michelle DeWitt has authored 7 papers receiving a total of 318 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Molecular Biology, 2 papers in General Health Professions and 1 paper in Surgery. Recurrent topics in Michelle DeWitt's work include Health Literacy and Information Accessibility (2 papers), Photoreceptor and optogenetics research (1 paper) and Bacterial Identification and Susceptibility Testing (1 paper). Michelle DeWitt is often cited by papers focused on Health Literacy and Information Accessibility (2 papers), Photoreceptor and optogenetics research (1 paper) and Bacterial Identification and Susceptibility Testing (1 paper). Michelle DeWitt collaborates with scholars based in United States and China. Michelle DeWitt's co-authors include Brian Coventry, David Baker, Gyu Rie Lee, Samer Halabiya, Declan Evans, Ivan Anishchenko, Longxing Cao, Justas Dauparas, K. N. Houk and Jason Z. Zhang and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Implementation Science.

In The Last Decade

Michelle DeWitt

6 papers receiving 312 citations

Hit Papers

align trajectories

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.

De novo design of luciferases using deep learning

2023 241 citations Hsien‐Wei Yeh, Christoffer Norn et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michelle DeWitt United States	6	201	49	38	35	27	7	318
Lingfei Yang China	9	112 0.6×	37 0.8×	26 0.7×	42 1.2×	9 0.3×	24	308
Divya Gopalan India	6	84 0.4×	84 1.7×	31 0.8×	9 0.3×	33 1.2×	9	303
Seong Won Kim United States	10	244 1.2×	24 0.5×	11 0.3×	21 0.6×	51 1.9×	24	390
Minhua Zhang China	12	148 0.7×	23 0.5×	29 0.8×	5 0.1×	15 0.6×	25	390
Gundula Bosch United States	9	159 0.8×	58 1.2×	35 0.9×	15 0.4×	18 0.7×	12	353
Damir Bojadzic United States	8	104 0.5×	22 0.4×	8 0.2×	7 0.2×	19 0.7×	8	299
Shemille A. Collingwood United States	6	229 1.1×	47 1.0×	11 0.3×	15 0.4×	32 1.2×	11	372
Anna Zhou United States	8	332 1.7×	24 0.5×	43 1.1×	10 0.3×	65 2.4×	20	583
Tiange Zhang China	9	83 0.4×	35 0.7×	57 1.5×	11 0.3×	20 0.7×	32	294
Elisa Costanzi Italy	13	138 0.7×	20 0.4×	20 0.5×	5 0.1×	5 0.2×	18	351

Countries citing papers authored by Michelle DeWitt

Since Specialization

Citations

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

Fields of papers citing papers by Michelle DeWitt

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michelle DeWitt

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

All Works

Sort: Min cites: Since: Top N: Style:

7 of 7 papers shown

1.

Yeh, Hsien‐Wei, Christoffer Norn, Yakov Kipnis, et al.. (2023). De novo design of luciferases using deep learning. Nature. 614(7949). 774–780. 241 indexed citations breakdown →

2.

Hicks, Derrick R., Michelle DeWitt, Brian Coventry, et al.. (2022). De novo design of protein homodimers containing tunable symmetric protein pockets. Proceedings of the National Academy of Sciences. 119(30). e2113400119–e2113400119. 9 indexed citations

3.

DeWitt, Michelle, et al.. (2020). Multiple Simultaneous Mature Teratomas of the Spinal Cord in an Adult. Cureus. 12(9). e10409–e10409.

4.

Davis, Matthew W., Dayna McManus, Alan Koff, et al.. (2020). Repurposing antimicrobial stewardship tools in the electronic medical record for the management of COVID-19 patients. Infection Control and Hospital Epidemiology. 41(11). 1335–1337. 10 indexed citations

5.

Bernstein, Steven L., Michelle DeWitt, Jeanette M. Tetrault, et al.. (2019). A randomized trial of decision support for tobacco dependence treatment in an inpatient electronic medical record: clinical results. Implementation Science. 14(1). 8–8. 15 indexed citations

6.

Bernstein, Steven L., Michelle DeWitt, Jeanette M. Tetrault, et al.. (2017). Design and implementation of decision support for tobacco dependence treatment in an inpatient electronic medical record: a randomized trial. Translational Behavioral Medicine. 7(2). 185–195. 33 indexed citations

7.

Bernstein, Steven L., et al.. (2015). Design and implementation of decision support for tobacco dependence treatment in an inpatient electronic medical record. Implementation Science. 10(S1). A1–I1. 10 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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