Eli J. Draizen

621 total citations
10 papers, 349 citations indexed

About

Eli J. Draizen is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Eli J. Draizen has authored 10 papers receiving a total of 349 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 2 papers in Computational Theory and Mathematics and 2 papers in Materials Chemistry. Recurrent topics in Eli J. Draizen's work include Protein Structure and Dynamics (6 papers), Machine Learning in Bioinformatics (5 papers) and RNA and protein synthesis mechanisms (3 papers). Eli J. Draizen is often cited by papers focused on Protein Structure and Dynamics (6 papers), Machine Learning in Bioinformatics (5 papers) and RNA and protein synthesis mechanisms (3 papers). Eli J. Draizen collaborates with scholars based in United States and Germany. Eli J. Draizen's co-authors include Philip E. Bourne, Paul B. Talbert, Alexey К. Shaytan, David Landsman, Leonardo Mariño‐Ramírez, Anna R. Panchenko, Lei Xie, Kelly P. Brock, Chris Sander and Christian Dallago and has published in prestigious journals such as Nature Communications, Bioinformatics and PLoS Biology.

In The Last Decade

Eli J. Draizen

10 papers receiving 345 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Eli J. Draizen United States	6	288	44	40	38	28	10	349
Elzbieta Rembeza Sweden	6	276 1.0×	23 0.5×	31 0.8×	34 0.9×	13 0.5×	7	354
Efrat Ben‐Zeev Israel	13	325 1.1×	32 0.7×	87 2.2×	51 1.3×	33 1.2×	24	439
Ayumu Muroya Japan	8	274 1.0×	83 1.9×	25 0.6×	34 0.9×	19 0.7×	15	378
Julien Henri France	12	355 1.2×	16 0.4×	23 0.6×	50 1.3×	31 1.1×	31	443
Ignacija Vlašić Croatia	12	314 1.1×	81 1.8×	21 0.5×	45 1.2×	17 0.6×	23	400
Alessio Del Conte Italy	6	237 0.8×	19 0.4×	30 0.8×	42 1.1×	11 0.4×	7	289
Lidan Sun China	15	293 1.0×	81 1.8×	31 0.8×	16 0.4×	43 1.5×	29	471
I. Reš United States	8	430 1.5×	46 1.0×	72 1.8×	94 2.5×	12 0.4×	8	469
Moon‐Hyeong Seo South Korea	11	380 1.3×	31 0.7×	29 0.7×	48 1.3×	38 1.4×	20	473
Min-Gang Su Taiwan	9	384 1.3×	15 0.3×	26 0.7×	18 0.5×	20 0.7×	11	450

Countries citing papers authored by Eli J. Draizen

Since Specialization

Citations

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

Fields of papers citing papers by Eli J. Draizen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eli J. Draizen. 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 Eli J. Draizen. The network helps show where Eli J. Draizen may publish in the future.

Co-authorship network of co-authors of Eli J. Draizen

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

All Works

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10 of 10 papers shown

1.

Draizen, Eli J., Stella Veretnik, Cameron Mura, & Philip E. Bourne. (2024). Deep generative models of protein structure uncover distant relationships across a continuous fold space. Nature Communications. 15(1). 8094–8094. 6 indexed citations

2.

Draizen, Eli J., et al.. (2024). Prop3D: A flexible, Python-based platform for machine learning with protein structural properties and biophysical data. BMC Bioinformatics. 25(1). 11–11. 1 indexed citations

3.

Bourne, Philip E., Eli J. Draizen, & Cameron Mura. (2022). The curse of the protein ribbon diagram. PLoS Biology. 20(12). e3001901–e3001901. 3 indexed citations

4.

Draizen, Eli J., et al.. (2020). Deep Learning of Protein Structural Classes: Any Evidence for an 'Urfold'?. Zenodo (CERN European Organization for Nuclear Research). 3 indexed citations

5.

Chen, Ruoyan, et al.. (2019). Machine Learning for Classification of Protein Helix Capping Motifs. 1–6. 1 indexed citations

6.

Hopf, Thomas A., Anna G. Green, Benjamin Schubert, et al.. (2018). The EVcouplings Python framework for coevolutionary sequence analysis. Bioinformatics. 35(9). 1582–1584. 172 indexed citations

7.

Mura, Cameron, Eli J. Draizen, & Philip E. Bourne. (2018). Structural biology meets data science: does anything change?. Current Opinion in Structural Biology. 52. 95–102. 6 indexed citations

8.

Moriarty, Nigel W., Eli J. Draizen, & Paul D. Adams. (2017). An editor for the generation and customization of geometry restraints. Acta Crystallographica Section D Structural Biology. 73(2). 123–130. 25 indexed citations

9.

Draizen, Eli J., Alexey К. Shaytan, Leonardo Mariño‐Ramírez, et al.. (2016). HistoneDB 2.0: a histone database with variants—an integrated resource to explore histones and their variants. Database. 2016. baw014–baw014. 96 indexed citations

10.

Xie, Lei, Eli J. Draizen, & Philip E. Bourne. (2016). Harnessing Big Data for Systems Pharmacology. The Annual Review of Pharmacology and Toxicology. 57(1). 245–262. 36 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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