Daniel Berenberg

1.6k total citations · 1 hit paper
6 papers, 582 citations indexed

About

Daniel Berenberg is a scholar working on Molecular Biology, Statistical and Nonlinear Physics and Computer Science Applications. According to data from OpenAlex, Daniel Berenberg has authored 6 papers receiving a total of 582 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Molecular Biology, 2 papers in Statistical and Nonlinear Physics and 2 papers in Computer Science Applications. Recurrent topics in Daniel Berenberg's work include Protein Structure and Dynamics (3 papers), Complex Network Analysis Techniques (2 papers) and Machine Learning in Bioinformatics (2 papers). Daniel Berenberg is often cited by papers focused on Protein Structure and Dynamics (3 papers), Complex Network Analysis Techniques (2 papers) and Machine Learning in Bioinformatics (2 papers). Daniel Berenberg collaborates with scholars based in United States, New Zealand and Poland. Daniel Berenberg's co-authors include Vladimir Gligorijević, Julia Koehler Leman, Richard Bonneau, Kyunghyun Cho, Ramnik J. Xavier, P. Douglas Renfrew, Tomasz Kościółek, Tommi Vatanen, Chris Chandler and Bryn C. Taylor and has published in prestigious journals such as Nature Communications, Nature Biotechnology and Proceedings of the ACM on Human-Computer Interaction.

In The Last Decade

Daniel Berenberg

6 papers receiving 575 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.

Structure-based protein function prediction using graph convolutional networks

2021 467 citations Vladimir Gligorijević, P. Douglas Renfrew et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Berenberg United States	4	479	118	65	35	34	6	582
I. Fisk United States	7	430 0.9×	116 1.0×	57 0.9×	32 0.9×	25 0.7×	26	608
Xuefeng Cui China	11	294 0.6×	110 0.9×	50 0.8×	23 0.7×	35 1.0×	40	428
Abhigyan Nath India	13	227 0.5×	150 1.3×	33 0.5×	55 1.6×	28 0.8×	43	482
Subu Subramanian United States	5	394 0.8×	62 0.5×	48 0.7×	48 1.4×	22 0.6×	8	542
Tom Gibbs United States	2	929 1.9×	188 1.6×	80 1.2×	61 1.7×	33 1.0×	4	1.1k
Ghalia Rehawi Germany	3	928 1.9×	188 1.6×	80 1.2×	61 1.7×	32 0.9×	4	1.1k
Ramzan Umarov Saudi Arabia	9	589 1.2×	80 0.7×	27 0.4×	33 0.9×	34 1.0×	12	716
Dan Ofer Israel	10	697 1.5×	110 0.9×	41 0.6×	95 2.7×	24 0.7×	17	881
Ben Krause United States	5	357 0.7×	62 0.5×	46 0.7×	68 1.9×	14 0.4×	22	547
Saad Haider United States	10	260 0.5×	71 0.6×	42 0.6×	16 0.5×	22 0.6×	18	489

Countries citing papers authored by Daniel Berenberg

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Berenberg

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Berenberg

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

All Works

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6 of 6 papers shown

1.

Hamamsy, Tymor, James T. Morton, Robert N. Blackwell, et al.. (2023). Protein remote homology detection and structural alignment using deep learning. Nature Biotechnology. 42(6). 975–985. 60 indexed citations

2.

Leman, Julia Koehler, P. Douglas Renfrew, Vladimir Gligorijević, et al.. (2023). Sequence-structure-function relationships in the microbial protein universe. Nature Communications. 14(1). 2351–2351. 44 indexed citations

3.

Gligorijević, Vladimir, P. Douglas Renfrew, Tomasz Kościółek, et al.. (2021). Structure-based protein function prediction using graph convolutional networks. Nature Communications. 12(1). 3168–3168. 467 indexed citations breakdown →

4.

Berenberg, Daniel, et al.. (2021). flatironinstitute/DeepFRI: First release. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

5.

Berenberg, Daniel & James P. Bagrow. (2018). Efficient Crowd Exploration of Large Networks. Proceedings of the ACM on Human-Computer Interaction. 2(CSCW). 1–25. 7 indexed citations

6.

Berenberg, Daniel & James P. Bagrow. (2018). Efficient Crowd Exploration of Large Networks: The Case of Causal Attribution. arXiv (Cornell University). 3 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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