Roded Sharan

22.2k total citations · 8 hit papers
214 papers, 14.0k citations indexed

About

Roded Sharan is a scholar working on Molecular Biology, Genetics and Computational Theory and Mathematics. According to data from OpenAlex, Roded Sharan has authored 214 papers receiving a total of 14.0k indexed citations (citations by other indexed papers that have themselves been cited), including 182 papers in Molecular Biology, 36 papers in Genetics and 31 papers in Computational Theory and Mathematics. Recurrent topics in Roded Sharan's work include Bioinformatics and Genomic Networks (107 papers), Gene expression and cancer classification (54 papers) and Microbial Metabolic Engineering and Bioproduction (41 papers). Roded Sharan is often cited by papers focused on Bioinformatics and Genomic Networks (107 papers), Gene expression and cancer classification (54 papers) and Microbial Metabolic Engineering and Bioproduction (41 papers). Roded Sharan collaborates with scholars based in Israel, United States and Germany. Roded Sharan's co-authors include Ron Shamir, Trey Ideker, Eytan Ruppin, Tomer Shlomi, Amos Tanay, Igor Ulitsky, Assaf Gottlieb, Richard M. Karp, Gideon Y. Stein and Martin Kupiec and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Roded Sharan

208 papers receiving 13.7k citations

Hit Papers

Network‐based prediction of protein function 2002 2026 2010 2018 2007 2010 2011 2008 2002 250 500 750
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. Network‐based prediction of protein function
    2007 764 citations Roded Sharan, Ron Shamir et al. profile →
  2. Associating Genes and Protein Complexes with Disease via Network Propagation
    2010 633 citations Eytan Ruppin, Roded Sharan et al. PLoS Computational Biology profile →
  3. PREDICT: a method for inferring novel drug indications with application to personalized medicine
    2011 623 citations Assaf Gottlieb, Eytan Ruppin et al. profile →
  4. Protein networks in disease
    2008 585 citations Trey Ideker, Roded Sharan profile →
  5. Discovering statistically significant biclusters in gene expression data
    2002 569 citations Roded Sharan, Ron Shamir et al. Bioinformatics profile →
  6. Conserved patterns of protein interaction in multiple species
    2005 507 citations Roded Sharan, Silpa Suthram et al. Proceedings of the National Academy of Sciences profile →
  7. Network propagation: a universal amplifier of genetic associations
    2017 400 citations Trey Ideker, Roded Sharan et al. profile →
  8. Competitive and cooperative metabolic interactions in bacterial communities
    2011 392 citations Shiri Freilich, E. Segal et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roded Sharan Israel 57 11.3k 3.0k 1.1k 1.1k 952 214 14.0k
Ron Shamir Israel 62 10.7k 0.9× 1.7k 0.6× 1.9k 1.7× 1.7k 1.6× 1.2k 1.3× 296 15.1k
Hiroaki Kitano Japan 52 8.7k 0.8× 1.5k 0.5× 1.0k 0.9× 1.6k 1.5× 531 0.6× 285 15.6k
Luonan Chen China 66 9.6k 0.8× 1.5k 0.5× 1.0k 0.9× 1.5k 1.3× 1.3k 1.3× 507 15.3k
Frederick P. Roth United States 49 9.9k 0.9× 1.1k 0.4× 1.8k 1.6× 300 0.3× 885 0.9× 133 11.9k
Yoshihiro Yamanishi Japan 33 6.7k 0.6× 3.1k 1.0× 661 0.6× 396 0.4× 674 0.7× 124 10.0k
Min Li China 53 7.3k 0.6× 2.8k 0.9× 300 0.3× 927 0.9× 1.4k 1.5× 489 10.8k
Hans‐Werner Mewes Germany 44 11.2k 1.0× 1.2k 0.4× 1.3k 1.2× 432 0.4× 515 0.5× 124 13.9k
Haiyuan Yu United States 42 8.2k 0.7× 1.4k 0.5× 963 0.8× 241 0.2× 430 0.5× 129 9.9k
Ioannis Xénarios Switzerland 52 10.3k 0.9× 1.2k 0.4× 1.2k 1.1× 371 0.3× 650 0.7× 171 14.3k
Zoltán N. Oltvai United States 41 17.4k 1.5× 2.4k 0.8× 1.8k 1.6× 773 0.7× 2.0k 2.1× 85 24.4k

Countries citing papers authored by Roded Sharan

Since Specialization
Citations

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

Fields of papers citing papers by Roded Sharan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roded Sharan

This figure shows the co-authorship network connecting the top 25 collaborators of Roded Sharan. A scholar is included among the top collaborators of Roded Sharan 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 Roded Sharan. Roded Sharan 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.
Kasif, Simon, et al.. (2024). SPIDER: constructing cell-type-specific protein–protein interaction networks. Bioinformatics Advances. 4(1). vbae130–vbae130. 3 indexed citations
2.
Leiserson, Mark D.M., et al.. (2023). A mutation-level covariate model for mutational signatures. PLoS Computational Biology. 19(6). e1011195–e1011195.
3.
Margalit, Sapir, Hila Sharim, Surajit Bhattacharya, et al.. (2021). Long reads capture simultaneous enhancer–promoter methylation status for cell-type deconvolution. Bioinformatics. 37(Supplement_1). i327–i333. 8 indexed citations
4.
Rauti, Rossana, Eyal Paz, Victoria J. Miller, et al.. (2021). Effect of SARS-CoV-2 proteins on vascular permeability. eLife. 10. 46 indexed citations
5.
Gale, Andrew N., et al.. (2020). Identification of Essential Genes and Fluconazole Susceptibility Genes in Candida glabrata by Profiling Hermes Transposon Insertions. G3 Genes Genomes Genetics. 10(10). 3859–3870. 27 indexed citations
6.
Kim, Yoo-Ah, Damian Wójtowicz, Welles Robinson, et al.. (2020). Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer. Genome Medicine. 12(1). 52–52. 18 indexed citations
7.
Segal, E., Bhawna Yadav, Jacob L. Steenwyk, et al.. (2018). Gene Essentiality Analyzed by In Vivo Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of Candida albicans. mBio. 9(5). 101 indexed citations
8.
Dao, Phuong, Yoo-Ah Kim, Damian Wójtowicz, et al.. (2017). BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions. PLoS Computational Biology. 13(10). e1005695–e1005695. 28 indexed citations
9.
Dao, Phuong, Yoo-Ah Kim, Sanna Madan, Roded Sharan, & Teresa M. Przytycka. (2017). BeWith: A Between-Within Method for Module Discovery in Cancer using Integrated Analysis of Mutual Exclusivity, Co-occurrence and Functional Interactions (Extended Abstract).. 370–371. 1 indexed citations
10.
Segev, Danny, et al.. (2013). The Approximability of Shortest Path-Based Graph Orientations of Protein–Protein Interaction Networks. Journal of Computational Biology. 20(12). 945–957. 3 indexed citations
11.
Silverbush, Dana, et al.. (2011). Optimally Orienting Physical Networks. Journal of Computational Biology. 18(11). 1437–1448. 15 indexed citations
12.
Atias, Nir & Roded Sharan. (2011). An Algorithmic Framework for Predicting Side Effects of Drugs. Journal of Computational Biology. 18(3). 207–218. 102 indexed citations
13.
Gottlieb, Assaf, et al.. (2011). Combining Drug and Gene Similarity Measures for Drug-Target Elucidation. Journal of Computational Biology. 18(2). 133–145. 162 indexed citations
14.
Freilich, Shiri, Anat Kreimer, Elhanan Borenstein, et al.. (2010). Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species. PLoS Computational Biology. 6(2). e1000690–e1000690. 29 indexed citations
15.
Waldman, Yedael Y., Tamir Tuller, Roded Sharan, & Eytan Ruppin. (2009). TP53 Cancerous Mutations Exhibit Selection for Translation Efficiency. Cancer Research. 69(22). 8807–8813. 12 indexed citations
16.
Yosef, Nir, Martin Kupiec, Eytan Ruppin, & Roded Sharan. (2009). A complex-centric view of protein network evolution. Nucleic Acids Research. 37(12). e88–e88. 21 indexed citations
17.
Freilich, Shiri, Anat Kreimer, Elhanan Borenstein, et al.. (2009). Metabolic-network-driven analysis of bacterial ecological strategies. Genome biology. 10(6). R61–R61. 82 indexed citations
18.
Sharan, Roded, Silpa Suthram, Ryan Kelley, et al.. (2005). Conserved patterns of protein interaction in multiple species. Proceedings of the National Academy of Sciences. 102(6). 1974–1979. 507 indexed citations breakdown →
19.
Sharan, Roded, Ivan Ovcharenko, Asa Ben‐Hur, & Richard M. Karp. (2003). CREME: a framework for identifying cis-regulatory modulesin human-mouse conserved segments. Bioinformatics. 19(suppl_1). i283–i291. 81 indexed citations
20.
Shamir, Ron & Roded Sharan. (2000). CLICK: A Clustering Algorithm for Gene Expression Analysis. 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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