Iddo Friedberg

14.0k total citations · 2 hit papers
59 papers, 7.2k citations indexed

About

Iddo Friedberg is a scholar working on Molecular Biology, Genetics and Materials Chemistry. According to data from OpenAlex, Iddo Friedberg has authored 59 papers receiving a total of 7.2k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Molecular Biology, 7 papers in Genetics and 7 papers in Materials Chemistry. Recurrent topics in Iddo Friedberg's work include Genomics and Phylogenetic Studies (24 papers), Bioinformatics and Genomic Networks (20 papers) and Machine Learning in Bioinformatics (12 papers). Iddo Friedberg is often cited by papers focused on Genomics and Phylogenetic Studies (24 papers), Bioinformatics and Genomic Networks (20 papers) and Machine Learning in Bioinformatics (12 papers). Iddo Friedberg collaborates with scholars based in United States, Israel and United Kingdom. Iddo Friedberg's co-authors include Adam Godzik, Brad Chapman, Michiel de Hoon, Peter Cock, Jeffrey T. Chang, Thomas Hamelryck, Andrew Dalke, Cymon J. Cox, Bartek Wilczyński and Tiago Antão and has published in prestigious journals such as Cell, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Iddo Friedberg

57 papers receiving 7.0k citations

Hit Papers

Biopython: freely available Python tools for computationa... 2004 2026 2011 2018 2009 2004 1000 2.0k 3.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Iddo Friedberg United States 25 5.7k 827 781 749 576 59 7.2k
Michiel de Hoon Japan 29 6.9k 1.2× 858 1.0× 841 1.1× 1.2k 1.6× 1.0k 1.7× 67 9.6k
Yanli Wang China 47 7.2k 1.3× 722 0.9× 699 0.9× 850 1.1× 989 1.7× 235 9.6k
Kevin Bryson United Kingdom 21 5.4k 1.0× 519 0.6× 530 0.7× 758 1.0× 617 1.1× 41 7.1k
Alper Küçükural United States 20 5.5k 1.0× 673 0.8× 374 0.5× 974 1.3× 750 1.3× 35 8.1k
Haim Ashkenazy Israel 23 4.7k 0.8× 423 0.5× 772 1.0× 1.1k 1.5× 890 1.5× 39 6.9k
Lim Heo United States 23 6.1k 1.1× 773 0.9× 712 0.9× 707 0.9× 667 1.2× 38 8.3k
Fabian Glaser Israel 34 4.6k 0.8× 334 0.4× 529 0.7× 707 0.9× 479 0.8× 76 6.2k
Yoshitaka Moriwaki Japan 13 3.7k 0.6× 402 0.5× 494 0.6× 615 0.8× 649 1.1× 32 5.5k
Dmitrij Frishman Germany 36 6.0k 1.1× 392 0.5× 479 0.6× 884 1.2× 501 0.9× 160 7.5k
Ambrish Roy United States 17 5.2k 0.9× 630 0.8× 430 0.6× 658 0.9× 758 1.3× 21 7.5k

Countries citing papers authored by Iddo Friedberg

Since Specialization
Citations

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

Fields of papers citing papers by Iddo Friedberg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Iddo Friedberg

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

All Works

20 of 20 papers shown
1.
Crécy‐Lagard, Valérie de, et al.. (2025). Limitations of current machine learning models in predicting enzymatic functions for uncharacterized proteins. G3 Genes Genomes Genetics. 15(10).
2.
Liu, Fang, Stephen C. Ekker, Karl J. Clark, et al.. (2025). Lineage labeling with zebrafish hand2 Cre and CreERT2 recombinase CRISPR knock‐ins. Developmental Dynamics. 255(1). 86–105. 2 indexed citations
3.
Banerjee, Priyanka, Oliver Eulenstein, & Iddo Friedberg. (2024). Discovering genomic islands in unannotated bacterial genomes using sequence embedding. Bioinformatics Advances. 4(1). vbae089–vbae089. 5 indexed citations
4.
Piovesan, Damiano, Alexander Miguel Monzón, Walter Reade, et al.. (2024). CAFA-evaluator: a Python tool for benchmarking ontological classification methods. Bioinformatics Advances. 4(1). vbae043–vbae043. 4 indexed citations
5.
Friedberg, Iddo, et al.. (2022). The field of protein function prediction as viewed by different domain scientists. Bioinformatics Advances. 2(1). vbac057–vbac057. 8 indexed citations
6.
Proctor, Alexandra, Jesse M. Hostetter, Naihui Zhou, et al.. (2022). Vertical transmission of attaching and invasive E. coli from the dam to neonatal mice predisposes to more severe colitis following exposure to a colitic insult later in life. PLoS ONE. 17(4). e0266005–e0266005. 7 indexed citations
7.
Friedberg, Iddo, et al.. (2020). Deploying MMEJ using MENdel in precision gene editing applications for gene therapy and functional genomics. Nucleic Acids Research. 49(1). 67–78. 10 indexed citations
8.
Hu, Xiao & Iddo Friedberg. (2019). SwiftOrtho: A fast, memory-efficient, multiple genome orthology classifier. GigaScience. 8(10). 23 indexed citations
9.
Nguyen, Huy, Ashish Jain, Oliver Eulenstein, & Iddo Friedberg. (2019). Tracing the ancestry of operons in bacteria. Bioinformatics. 35(17). 2998–3004. 8 indexed citations
10.
Wierson, Wesley A., Carla M. Mann, Jordan M. Welker, et al.. (2019). Expanding the CRISPR Toolbox with ErCas12a in Zebrafish and Human Cells. The CRISPR Journal. 2(6). 417–433. 35 indexed citations
11.
Wimalanathan, Kokulapalan, Iddo Friedberg, Carson M. Andorf, & Carolyn J. Lawrence‐Dill. (2018). Maize GO Annotation—Methods, Evaluation, and Review (maize‐GAMER). Plant Direct. 2(4). e00052–e00052. 77 indexed citations
12.
Hamid, Md-Nafiz & Iddo Friedberg. (2018). Identifying antimicrobial peptides using word embedding with deep recurrent neural networks. Bioinformatics. 35(12). 2009–2016. 88 indexed citations
13.
Kacsoh, Balint Z, S.K. Barton, Yuxiang Jiang, et al.. (2018). New Drosophila Long-Term Memory Genes Revealed by Assessing Computational Function Prediction Methods. G3 Genes Genomes Genetics. 9(1). 251–267. 13 indexed citations
14.
Schnoes, Alexandra M., et al.. (2013). Biases in the Experimental Annotations of Protein Function and Their Effect on Our Understanding of Protein Function Space. PLoS Computational Biology. 9(5). e1003063–e1003063. 98 indexed citations
15.
Donovan, Sharon M., Mei Wang, Min Li, et al.. (2012). Host-Microbe Interactions in the Neonatal Intestine: Role of Human Milk Oligosaccharides. Advances in Nutrition. 3(3). 450S–455S. 82 indexed citations
16.
Grant, Barry J., et al.. (2007). The 2006 Automated Function Prediction Meeting. BMC Bioinformatics. 8(S4). 8 indexed citations
17.
Friedberg, Iddo. (2006). Automated protein function prediction--the genomic challenge. Briefings in Bioinformatics. 7(3). 225–242. 256 indexed citations
18.
Friedberg, Iddo, Lukasz Jaroszewski, Yuzhen Ye, & Adam Godzik. (2004). The interplay of fold recognition and experimental structure determination in structural genomics. Current Opinion in Structural Biology. 14(3). 307–312. 28 indexed citations
19.
Hoon, Michiel de, Brad Chapman, & Iddo Friedberg. (2003). Bioinformatics and Computational Biology with Biopython. Proceedings Genome Informatics Workshop/Genome informatics. 14. 298–299. 4 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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