Uri Keich

2.0k total citations
58 papers, 1.2k citations indexed

About

Uri Keich is a scholar working on Molecular Biology, Spectroscopy and Artificial Intelligence. According to data from OpenAlex, Uri Keich has authored 58 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Molecular Biology, 19 papers in Spectroscopy and 9 papers in Artificial Intelligence. Recurrent topics in Uri Keich's work include Advanced Proteomics Techniques and Applications (18 papers), Mass Spectrometry Techniques and Applications (17 papers) and Genomics and Phylogenetic Studies (10 papers). Uri Keich is often cited by papers focused on Advanced Proteomics Techniques and Applications (18 papers), Mass Spectrometry Techniques and Applications (17 papers) and Genomics and Phylogenetic Studies (10 papers). Uri Keich collaborates with scholars based in United States, Australia and Israel. Uri Keich's co-authors include Pavel A. Pevzner, William Stafford Noble, Yanni Sun, Jeremy Buhler, Attila Kertész‐Farkas, Nuno Bandeira, Nitin Gupta, Niranjan Nagarajan, Bin Ma and Ming Li and has published in prestigious journals such as Journal of the American Statistical Association, Bioinformatics and Nature Methods.

In The Last Decade

Uri Keich

56 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Uri Keich United States	18	953	286	241	112	78	58	1.2k
Michael Brown United States	9	1.4k 1.5×	64 0.2×	413 1.7×	177 1.6×	195 2.5×	10	2.0k
Kiyoko F. Aoki‐Kinoshita Japan	28	2.3k 2.4×	342 1.2×	125 0.5×	109 1.0×	88 1.1×	112	2.7k
Nicole Redaschi Switzerland	14	1.0k 1.1×	96 0.3×	102 0.4×	126 1.1×	89 1.1×	29	1.3k
Nico Pfeifer Germany	20	1.1k 1.1×	655 2.3×	85 0.4×	79 0.7×	39 0.5×	64	1.9k
Kei-Hoi Cheung United States	11	759 0.8×	105 0.4×	70 0.3×	54 0.5×	63 0.8×	21	943
Tetsuhiro Ogawa Japan	18	675 0.7×	23 0.1×	209 0.9×	222 2.0×	72 0.9×	46	1.1k
Maude Pupin France	13	590 0.6×	48 0.2×	39 0.2×	45 0.4×	171 2.2×	26	835
Pieter Meysman Belgium	23	910 1.0×	105 0.4×	75 0.3×	87 0.8×	34 0.4×	80	1.5k
Zhirong Sun China	21	1.8k 1.9×	76 0.3×	127 0.5×	87 0.8×	111 1.4×	55	2.3k
Rama Balakrishnan United States	14	1.4k 1.5×	49 0.2×	91 0.4×	139 1.2×	158 2.0×	24	1.6k

Countries citing papers authored by Uri Keich

Since Specialization

Citations

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

Fields of papers citing papers by Uri Keich

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Uri Keich

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

All Works

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20 of 20 papers shown

Käll, Lukas, et al.. (2025). How to Train a Postprocessor for Tandem Mass Spectrometry Proteomics Database Search While Maintaining Control of the False Discovery Rate. Journal of Proteome Research. 24(5). 2266–2279. 2 indexed citations

Wen, Bo, et al.. (2025). Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment. Nature Methods. 22(7). 1454–1463. 5 indexed citations

Noble, William Stafford, et al.. (2024). A BLAST from the past: revisiting blastp’s E-value. Bioinformatics. 40(12).

Noble, William Stafford, et al.. (2023). Competition-Based Control of the False Discovery Proportion. Biometrics. 79(4). 3472–3484. 1 indexed citations

Noble, William Stafford, et al.. (2022). Group-walk: a rigorous approach to group-wise false discovery rate analysis by target-decoy competition. Bioinformatics. 38(Supplement_2). ii82–ii88. 8 indexed citations

Lin, Andy, et al.. (2022). Improving Peptide-Level Mass Spectrometry Analysis via Double Competition. Journal of Proteome Research. 21(10). 2412–2420. 17 indexed citations

Lin, Andy, Deanna L. Plubell, Uri Keich, & William Stafford Noble. (2021). Accurately Assigning Peptides to Spectra When Only a Subset of Peptides Are Relevant. Journal of Proteome Research. 20(8). 4153–4164. 9 indexed citations

Noble, William Stafford & Uri Keich. (2017). Response to “Mass spectrometrists should search for all peptides, but assess only the ones they care about”. Nature Methods. 14(7). 644–644. 7 indexed citations

Liachko, Ivan, et al.. (2012). High-resolution mapping, characterization, and optimization of autonomously replicating sequences in yeast. Genome Research. 23(4). 698–704. 45 indexed citations

10.

Gupta, Nitin, Nuno Bandeira, Uri Keich, & Pavel A. Pevzner. (2011). Target-Decoy Approach and False Discovery Rate: When Things May Go Wrong. Journal of the American Society for Mass Spectrometry. 22(7). 1111–1120. 122 indexed citations

11.

Bhaskar, Anand & Uri Keich. (2010). Confidently Estimating the Number of DNA Replication Origins. Statistical Applications in Genetics and Molecular Biology. 9(1). Article28–Article28. 3 indexed citations

12.

Liachko, Ivan, et al.. (2010). A Comprehensive Genome-Wide Map of Autonomously Replicating Sequences in a Naive Genome. PLoS Genetics. 6(5). e1000946–e1000946. 44 indexed citations

13.

Oliver, Haley F., Renato H. Orsi, Lalit Ponnala, et al.. (2009). Deep RNA sequencing of L. monocytogenes reveals overlapping and extensive stationary phase and sigma B-dependent transcriptomes, including multiple highly transcribed noncoding RNAs. BMC Genomics. 10(1). 641–641. 139 indexed citations

14.

Keich, Uri, Hong Gao, Anand Bhaskar, et al.. (2008). Computational detection of significant variation in binding affinity across two sets of sequences with application to the analysis of replication origins in yeast. BMC Bioinformatics. 9(1). 372–372. 6 indexed citations

15.

Zhi, Degui, Uri Keich, Pavel A. Pevzner, Steffen Heber, & Haixu Tang. (2007). Correcting Base-Assignment Errors in Repeat Regions of Shotgun Assembly. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 4(1). 54–64. 7 indexed citations

16.

Buhler, Jeremy, Uri Keich, & Yanni Sun. (2005). Designing seeds for similarity search in genomic DNA. Journal of Computer and System Sciences. 70(3). 342–363. 70 indexed citations

17.

Keich, Uri, Ming Li, Bin Ma, & John Tromp. (2003). On spaced seeds for similarity search. Discrete Applied Mathematics. 138(3). 253–263. 87 indexed citations

18.

Keich, Uri & Pavel A. Pevzner. (2002). Subtle motifs: defining the limits of motif finding algorithms. Bioinformatics. 18(10). 1382–1390. 40 indexed citations

19.

Keich, Uri. (1999). Absolute continuity between the Wiener and stationary Gaussian measures. Pacific Journal of Mathematics. 188(1). 95–108. 2 indexed citations

20.

Aharoni, Ron & Uri Keich. (1996). A generalization of the Ahlswede-Daykin inequality. Discrete Mathematics. 152(1-3). 1–12. 6 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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