Avi Ma’ayan
Impact in
- Molecular Biology top 0.1%
- Bioinformatics and Genomic Networks
- Pluripotent Stem Cells Research
- Genomics and Chromatin Dynamics
- Genetics top 0.5%
- Genetics and Neurodevelopmental Disorders
- Mesenchymal stem cell research
Papers in
-
- Bioinformatics and Genomic Networks 79
- Gene Regulatory Network Analysis 19
- Biomedical Text Mining and Ontologies 12
- Pluripotent Stem Cells Research 12
- Gene expression and cancer classification 11
-
- Computational Drug Discovery Methods 25
- Co-authors
- Zichen Wang (38 shared papers)Alexander Lachmann (40 shared papers)Qiaonan Duan (14 shared papers)Kathleen M. Jagodnik (16 shared papers)Neil R. Clark (21 shared papers)Sherry L. Jenkins (19 shared papers)Edward Y. Chen (13 shared papers)Yan Kou (12 shared papers)
- Journals
- Nucleic Acids Research (17 papers)Bioinformatics (14 papers)BMC Bioinformatics (8 papers)Science Signaling (7 papers)Nature Communications (6 papers)
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Avi Ma’ayan
219 papers receiving 40.8k citations
Avi Ma’ayan's Hit Papers
Peers
Comparison fields: 5 of 191
- Molecular Biology 14.2k
- Genetics 1.8k
- Hematology 2.4k
- Cancer Research 2.3k
- Immunology 3.2k
Countries citing papers authored by Avi Ma’ayan
This map shows the geographic impact of Avi Ma’ayan'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 Avi Ma’ayan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Avi Ma’ayan more than expected).
Fields of papers citing papers by Avi Ma’ayan
This network shows the impact of papers produced by Avi Ma’ayan. 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 Avi Ma’ayan. The network helps show where Avi Ma’ayan may publish in the future.
Co-authors
The 25 scholars most cited alongside Avi Ma’ayan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 223 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Enrichr: a comprehensive gene set enrichment analysis web server 2016 update Hit paper breakdown → | 2016 | 7399 |
| 2 | Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool Hit paper breakdown → | 2013 | 5576 |
| 3 | Mesenchymal and haematopoietic stem cells form a unique bone marrow niche Hit paper breakdown → | 2010 | 2779 |
| 4 | Gene Set Knowledge Discovery with Enrichr Hit paper breakdown → | 2021 | 2283 |
| 5 | Synaptic, transcriptional and chromatin genes disrupted in autism Hit paper breakdown → | 2014 | 2115 |
| 6 | Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages Hit paper breakdown → | 2012 | 1662 |
| 7 | Patterns and rates of exonic de novo mutations in autism spectrum disorders Hit paper breakdown → | 2012 | 1386 |
| 8 | The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins Hit paper breakdown → | 2016 | 1150 |
| 9 | Functional atlas of the integrin adhesome Hit paper breakdown → | 2007 | 952 |
| 10 | ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments Hit paper breakdown → | 2010 | 784 |
| 11 | Single-cell immune landscape of human atherosclerotic plaques Hit paper breakdown → | 2019 | 735 |
| 12 | ChEA3: transcription factor enrichment analysis by orthogonal omics integration Hit paper breakdown → | 2019 | 730 |
| 13 | Minimal Differentiation of Classical Monocytes as They Survey Steady-State Tissues and Transport Antigen to Lymph Nodes Hit paper breakdown → | 2013 | 635 |
| 14 | Massive mining of publicly available RNA-seq data from human and mouse Hit paper breakdown → | 2018 | 483 |
| 15 | Differential cytokine contributions of perivascular haematopoietic stem cell niches Hit paper breakdown → | 2017 | 345 |
| 16 | 2009 | 336 | |
| 17 | 2017 | 309 | |
| 18 | 2009 | 291 | |
| 19 | Unexplored therapeutic opportunities in the human genome Hit paper breakdown → | 2018 | 275 |
| 20 | 2016 | 262 |
About Avi Ma’ayan
Avi Ma’ayan is a scholar working on Molecular Biology, Computational Theory and Mathematics, Nephrology, Hematology and Information Systems and Management, having authored 223 papers that have together received 41.2k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (79 papers), Computational Drug Discovery Methods (25 papers), Gene Regulatory Network Analysis (19 papers), Biomedical Text Mining and Ontologies (12 papers), Pluripotent Stem Cells Research (12 papers), Gene expression and cancer classification (11 papers), Renal Diseases and Glomerulopathies (10 papers) and Hematopoietic Stem Cell Transplantation (8 papers). The work is most often cited by research in Molecular Biology (14.2k citations), Genetics (1.8k citations), Hematology (2.4k citations), Cancer Research (2.3k citations) and Immunology (3.2k citations). Avi Ma’ayan has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Zichen Wang, Alexander Lachmann, Qiaonan Duan, Kathleen M. Jagodnik, Neil R. Clark, Sherry L. Jenkins, Edward Y. Chen, Yan Kou, Maxim V. Kuleshov and Nicolas Fernandez. Their work appears in journals such as Nucleic Acids Research, Bioinformatics, BMC Bioinformatics, Science Signaling and Nature Communications.
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.