Nir Friedman
Impact in
- Molecular Biology top 0.1%
- Bioinformatics and Genomic Networks
- Gene Regulatory Network Analysis
- Gene expression and cancer classification
- Genomics and Chromatin Dynamics
- RNA and protein synthesis mechanisms
- RNA Research and Splicing
- Single-cell and spatial transcriptomics
- Artificial Intelligence top 0.05%
- Bayesian Modeling and Causal Inference
Papers in
-
- Genomics and Chromatin Dynamics 29
- Gene Regulatory Network Analysis 28
- Bioinformatics and Genomic Networks 23
- Gene expression and cancer classification 22
- RNA and protein synthesis mechanisms 19
- RNA Research and Splicing 14
-
- Bayesian Modeling and Causal Inference 51
- Logic, Reasoning, and Knowledge 15
- Co-authors
- Daniel L. Koller (1 shared paper)Moisés Goldszmidt (8 shared papers)Dan Geiger (2 shared papers)Daphne Koller (22 shared papers)Aviv Regev (23 shared papers)Dana Pe’er (7 shared papers)Iftach Nachman (9 shared papers)Michal Linial (2 shared papers)
- Journals
- Bioinformatics (16 papers)Journal of Computational Biology (7 papers)Proceedings of the National Academy of Sciences (6 papers)Nature (5 papers)PLoS Biology (5 papers)
- Partner nations
- IsraelUnited StatesGermany
In The Last Decade
Nir Friedman
202 papers receiving 29.2k citations
Nir Friedman's Hit Papers
Peers
Comparison fields: 5 of 220
- Molecular Biology 17.5k
- Artificial Intelligence 7.8k
- Biophysics 837
- Aging 241
- Signal Processing 1.2k
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 210 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Probabilistic graphical models : principles and techniques Hit paper breakdown → | 2009 | 3712 |
| 2 | Bayesian Network Classifiers Hit paper breakdown → | 1997 | 3302 |
| 3 | Using Bayesian Networks to Analyze Expression Data Hit paper breakdown → | 2000 | 2101 |
| 4 | Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data Hit paper breakdown → | 2003 | 1158 |
| 5 | Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens Hit paper breakdown → | 2016 | 1027 |
| 6 | Stochastic protein expression in individual cells at the single molecule level Hit paper breakdown → | 2006 | 853 |
| 7 | Paternally Induced Transgenerational Environmental Reprogramming of Metabolic Gene Expression in Mammals Hit paper breakdown → | 2010 | 836 |
| 8 | Inferring Cellular Networks Using Probabilistic Graphical Models Hit paper breakdown → | 2004 | 823 |
| 9 | Chromatin state dynamics during blood formation Hit paper breakdown → | 2014 | 556 |
| 10 | Tissue Classification with Gene Expression Profiles Hit paper breakdown → | 2000 | 537 |
| 11 | Comprehensive comparative analysis of strand-specific RNA sequencing methods Hit paper breakdown → | 2010 | 533 |
| 12 | A module map showing conditional activity of expression modules in cancer Hit paper breakdown → | 2004 | 524 |
| 13 | Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks Hit paper breakdown → | 2003 | 511 |
| 14 | 2006 | 491 | |
| 15 | Mapping Nucleosome Resolution Chromosome Folding in Yeast by Micro-C Hit paper breakdown → | 2015 | 490 |
| 16 | 2007 | 480 | |
| 17 | 2007 | 435 | |
| 18 | 2011 | 414 | |
| 19 | 2005 | 401 | |
| 20 | Wishbone identifies bifurcating developmental trajectories from single-cell data Hit paper breakdown → | 2016 | 384 |
About Nir Friedman
Nir Friedman is a scholar working on Molecular Biology, Artificial Intelligence, Atomic and Molecular Physics, and Optics, Plant Science and Signal Processing, having authored 210 papers that have together received 30.5k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (51 papers), Genomics and Chromatin Dynamics (29 papers), Gene Regulatory Network Analysis (28 papers), Bioinformatics and Genomic Networks (23 papers), Gene expression and cancer classification (22 papers), RNA and protein synthesis mechanisms (19 papers), Logic, Reasoning, and Knowledge (15 papers) and RNA Research and Splicing (14 papers). The work is most often cited by research in Molecular Biology (17.5k citations), Artificial Intelligence (7.8k citations), Biophysics (837 citations), Aging (241 citations) and Signal Processing (1.2k citations). Nir Friedman has collaborated with scholars based in Israel, United States and Germany. Frequent co-authors include Daniel L. Koller, Moisés Goldszmidt, Dan Geiger, Daphne Koller, 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, Proceedings of the National Academy of Sciences, Nature and PLoS Biology.
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.