Jan Ihmels

5.7k total citations · 3 hit papers
13 papers, 4.0k citations indexed

About

Jan Ihmels is a scholar working on Molecular Biology, Cell Biology and Biomedical Engineering. According to data from OpenAlex, Jan Ihmels has authored 13 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 1 paper in Cell Biology and 1 paper in Biomedical Engineering. Recurrent topics in Jan Ihmels's work include Bioinformatics and Genomic Networks (10 papers), Gene expression and cancer classification (6 papers) and Fungal and yeast genetics research (4 papers). Jan Ihmels is often cited by papers focused on Bioinformatics and Genomic Networks (10 papers), Gene expression and cancer classification (6 papers) and Fungal and yeast genetics research (4 papers). Jan Ihmels collaborates with scholars based in Israel, United States and Estonia. Jan Ihmels's co-authors include Naama Barkai, Sven Bergmann, Jonathan S. Weissman, Sina Ghaemmaghami, David K. Breslow, J. Robert Newman, Joseph L. DeRisi, Gilgi Friedlander, Ofer Sarig and Yaniv Ziv and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Jan Ihmels

13 papers receiving 3.9k citations

Hit Papers

Single-cell proteomic analysis of S. cerevisiae reveals t... 2002 2026 2010 2018 2006 2005 2002 250 500 750 1000
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.

  1. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise
    2006 1.2k citations J. Robert Newman, Sina Ghaemmaghami et al. Nature profile →
  2. Exploration of the Function and Organization of the Yeast Early Secretory Pathway through an Epistatic Miniarray Profile
    2005 683 citations Maya Schuldiner, Sean R. Collins et al. Cell profile →
  3. Revealing modular organization in the yeast transcriptional network
    2002 520 citations Jan Ihmels, Gilgi Friedlander et al. Nature Genetics profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jan Ihmels Israel 13 3.6k 591 281 240 180 13 4.0k
Herbert M. Sauro United States 36 3.8k 1.1× 495 0.8× 276 1.0× 160 0.7× 239 1.3× 136 4.3k
Julien Gagneur Germany 37 5.0k 1.4× 824 1.4× 205 0.7× 455 1.9× 101 0.6× 92 6.0k
Timothy Galitski United States 21 3.2k 0.9× 603 1.0× 231 0.8× 487 2.0× 111 0.6× 33 3.9k
Edda Klipp Germany 39 4.4k 1.2× 358 0.6× 404 1.4× 362 1.5× 215 1.2× 161 5.4k
Frank J. Bruggeman Netherlands 38 4.0k 1.1× 712 1.2× 151 0.5× 196 0.8× 180 1.0× 124 4.9k
Luís Serrano Spain 33 3.7k 1.0× 396 0.7× 293 1.0× 134 0.6× 63 0.3× 66 4.3k
Kirk R. Anders United States 10 4.1k 1.1× 319 0.5× 387 1.4× 293 1.2× 68 0.4× 11 4.4k
Wenzhe Ma China 30 2.4k 0.7× 322 0.5× 368 1.3× 310 1.3× 181 1.0× 111 3.7k
Hana El‐Samad United States 35 3.7k 1.0× 514 0.9× 732 2.6× 299 1.2× 302 1.7× 78 4.7k
Séan O’Donoghue Australia 29 3.3k 0.9× 329 0.6× 302 1.1× 175 0.7× 183 1.0× 69 4.2k

Countries citing papers authored by Jan Ihmels

Since Specialization
Citations

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

Fields of papers citing papers by Jan Ihmels

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jan Ihmels

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

All Works

13 of 13 papers shown
1.
Levy, Sagi, Jan Ihmels, Miri Carmi, et al.. (2007). Strategy of Transcription Regulation in the Budding Yeast. PLoS ONE. 2(2). e250–e250. 61 indexed citations
2.
Ihmels, Jan, Sean R. Collins, Maya Schuldiner, Nevan J. Krogan, & Jonathan S. Weissman. (2007). Backup without redundancy: genetic interactions reveal the cost of duplicate gene loss. Molecular Systems Biology. 3(1). 86–86. 121 indexed citations
3.
Newman, J. Robert, Sina Ghaemmaghami, Jan Ihmels, et al.. (2006). Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature. 441(7095). 840–846. 1173 indexed citations breakdown →
4.
Ihmels, Jan, Sven Bergmann, Maryam Gerami‐Nejad, et al.. (2005). Rewiring of the Yeast Transcriptional Network Through the Evolution of Motif Usage. Science. 309(5736). 938–940. 221 indexed citations
5.
Schuldiner, Maya, Sean R. Collins, Natalie Thompson, et al.. (2005). Exploration of the Function and Organization of the Yeast Early Secretory Pathway through an Epistatic Miniarray Profile. Cell. 123(3). 507–519. 683 indexed citations breakdown →
6.
Ihmels, Jan, Sven Bergmann, Judith Berman, & Naama Barkai. (2005). Comparative Gene Expression Analysis by a Differential Clustering Approach: Application to the Candida albicans Transcription Program. PLoS Genetics. 1(3). e39–e39. 108 indexed citations
7.
Kapushesky, Misha, Patrick Kemmeren, Aedín C. Culhane, et al.. (2004). Expression Profiler: next generation--an online platform for analysis of microarray data. Nucleic Acids Research. 32(Web Server). W465–W470. 94 indexed citations
8.
Ihmels, Jan. (2004). Challenges and prospects in the analysis of large-scale gene expression data. Briefings in Bioinformatics. 5(4). 313–327. 12 indexed citations
9.
Ihmels, Jan, Sven Bergmann, & Naama Barkai. (2004). Defining transcription modules using large-scale gene expression data. Bioinformatics. 20(13). 1993–2003. 236 indexed citations
10.
Bergmann, Sven, Jan Ihmels, & Naama Barkai. (2003). Iterative signature algorithm for the analysis of large-scale gene expression data. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 67(3). 31902–31902. 288 indexed citations
11.
Ihmels, Jan, Ronen Levy, & Naama Barkai. (2003). Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiae. Nature Biotechnology. 22(1). 86–92. 194 indexed citations
12.
Bergmann, Sven, Jan Ihmels, & Naama Barkai. (2003). Similarities and Differences in Genome-Wide Expression Data of Six Organisms. PLoS Biology. 2(1). e9–e9. 271 indexed citations
13.
Ihmels, Jan, Gilgi Friedlander, Sven Bergmann, et al.. (2002). Revealing modular organization in the yeast transcriptional network. Nature Genetics. 31(4). 370–377. 520 indexed citations breakdown →

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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