Nir Friedman
Impact in
- Molecular Biology top 0.05%
- Gene Regulatory Network Analysis
- Bioinformatics and Genomic Networks
- Genomics and Phylogenetic Studies
- Gene expression and cancer classification
- Genomics and Chromatin Dynamics
- Single-cell and spatial transcriptomics
- RNA and protein synthesis mechanisms
- Artificial Intelligence top 0.05%
- Bayesian Modeling and Causal Inference
Papers in
-
- Genomics and Chromatin Dynamics 32
- Gene Regulatory Network Analysis 26
- RNA and protein synthesis mechanisms 22
- Gene expression and cancer classification 21
- Bioinformatics and Genomic Networks 21
- Fungal and yeast genetics research 15
- Single-cell and spatial transcriptomics 14
-
- Bayesian Modeling and Causal Inference 44
- Co-authors
- Daniel L. Koller (1 shared paper)Moisés Goldszmidt (7 shared papers)Daphne Koller (22 shared papers)Dan Geiger (2 shared papers)Aviv Regev (22 shared papers)Dana Pe’er (5 shared papers)Iftach Nachman (7 shared papers)Michal Linial (2 shared papers)
- Journals
- Bioinformatics (15 papers)Journal of Computational Biology (7 papers)Nature (7 papers)Proceedings of the National Academy of Sciences (6 papers)Science (6 papers)
- Partner nations
- IsraelUnited StatesGermany
In The Last Decade
Nir Friedman
212 papers receiving 57.7k citations
Nir Friedman's Hit Papers
Peers
Comparison fields: 5 of 225
- Molecular Biology 29.2k
- Artificial Intelligence 9.6k
- Genetics 6.2k
- Plant Science 8.1k
- Aging 361
Countries citing papers authored by Nir Friedman
This map shows the geographic impact of Nir Friedman'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 Nir Friedman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nir Friedman more than expected).
Fields of papers citing papers by Nir Friedman
This network shows the impact of papers produced by Nir Friedman. 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 Nir Friedman. The network helps show where Nir Friedman may publish in the future.
Co-authors
The 25 scholars most cited alongside Nir Friedman, 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 218 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Full-length transcriptome assembly from RNA-Seq data without a reference genome Hit paper breakdown → | 2011 | 16340 |
| 2 | De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis Hit paper breakdown → | 2013 | 6311 |
| 3 | Probabilistic graphical models : principles and techniques Hit paper breakdown → | 2009 | 4325 |
| 4 | Bayesian Network Classifiers Hit paper breakdown → | 1997 | 3897 |
| 5 | Using Bayesian Networks to Analyze Expression Data Hit paper breakdown → | 2000 | 2392 |
| 6 | Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data Hit paper breakdown → | 2003 | 1253 |
| 7 | Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens Hit paper breakdown → | 2016 | 1172 |
| 8 | Paternally Induced Transgenerational Environmental Reprogramming of Metabolic Gene Expression in Mammals Hit paper breakdown → | 2010 | 937 |
| 9 | Stochastic protein expression in individual cells at the single molecule level Hit paper breakdown → | 2006 | 896 |
| 10 | Inferring Cellular Networks Using Probabilistic Graphical Models Hit paper breakdown → | 2004 | 880 |
| 11 | Single-cell RNA-seq reveals dynamic paracrine control of cellular variation Hit paper breakdown → | 2014 | 771 |
| 12 | Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis Hit paper breakdown → | 2011 | 743 |
| 13 | Learning Probabilistic Relational Models Hit paper breakdown → | 2001 | 691 |
| 14 | 2000 | 627 | |
| 15 | Chromatin state dynamics during blood formation Hit paper breakdown → | 2014 | 610 |
| 16 | 2010 | 588 | |
| 17 | 2003 | 584 | |
| 18 | 2004 | 557 | |
| 19 | Mapping Nucleosome Resolution Chromosome Folding in Yeast by Micro-C Hit paper breakdown → | 2015 | 523 |
| 20 | 2006 | 507 |
About Nir Friedman
Nir Friedman is a scholar working on Molecular Biology, Artificial Intelligence, Signal Processing, Biophysics and Statistics and Probability, having authored 218 papers that have together received 59.3k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (44 papers), Genomics and Chromatin Dynamics (32 papers), Gene Regulatory Network Analysis (26 papers), RNA and protein synthesis mechanisms (22 papers), Gene expression and cancer classification (21 papers), Bioinformatics and Genomic Networks (21 papers), Fungal and yeast genetics research (15 papers) and Single-cell and spatial transcriptomics (14 papers). The work is most often cited by research in Molecular Biology (29.2k citations), Artificial Intelligence (9.6k citations), Genetics (6.2k citations), Plant Science (8.1k citations) and Aging (361 citations). Nir Friedman has collaborated with scholars based in Israel, United States and Germany. Frequent co-authors include Daniel L. Koller, Moisés Goldszmidt, Daphne Koller, Dan Geiger, Aviv Regev, Dana Pe’er, Iftach Nachman, Michal Linial, Long Cai and Eran Segal. Their work appears in journals such as Bioinformatics, Journal of Computational Biology, Nature, Proceedings of the National Academy of Sciences and Science.
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.