| Full-length transcriptome assembly from RNA-Seq data without a reference genome MG Grabherr, BJ Haas, M Yassour, JZ Levin, DA Thompson, I Amit, ... Nature biotechnology 29 (7), 644-652, 2011 | 22124* | 2011 |
| Probabilistic graphical models: principles and techniques D Koller, N Friedman MIT press, 2009 | 12734 | 2009 |
| De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis BJ Haas, A Papanicolaou, M Yassour, M Grabherr, PD Blood, J Bowden, ... Nature protocols 8 (8), 1494-1512, 2013 | 8996 | 2013 |
| Bayesian network classifiers N Friedman, D Geiger, M Goldszmidt Machine learning 29 (2), 131-163, 1997 | 7755 | 1997 |
| Using Bayesian networks to analyze expression data N Friedman, M Linial, I Nachman, D Pe'er Proceedings of the fourth annual international conference on Computational …, 2000 | 4769 | 2000 |
| Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data E Segal, M Shapira, A Regev, D Pe'er, D Botstein, D Koller, N Friedman Nature genetics 34 (2), 166-176, 2003 | 2264 | 2003 |
| Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens A Dixit, O Parnas, B Li, J Chen, CP Fulco, L Jerby-Arnon, ND Marjanovic, ... cell 167 (7), 1853-1866. e17, 2016 | 1970 | 2016 |
| Inferring cellular networks using probabilistic graphical models N Friedman Science 303 (5659), 799, 2004 | 1682 | 2004 |
| Image segmentation in video sequences: A probabilistic approach N Friedman, S Russell arXiv preprint arXiv:1302.1539, 2013 | 1522 | 2013 |
| Paternally induced transgenerational environmental reprogramming of metabolic gene expression in mammals BR Carone, L Fauquier, N Habib, JM Shea, CE Hart, R Li, C Bock, C Li, ... Cell 143 (7), 1084-1096, 2010 | 1380 | 2010 |
| Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks N Friedman, D Koller Machine learning 50 (1), 95-125, 2003 | 1253 | 2003 |
| Learning probabilistic relational models N Friedman, L Getoor, D Koller, A Pfeffer IJCAI 99, 1300-1309, 1999 | 1217 | 1999 |
| Single-cell RNA-seq reveals dynamic paracrine control of cellular variation AK Shalek, R Satija, J Shuga, JJ Trombetta, D Gennert, D Lu, P Chen, ... Nature 510 (7505), 363-369, 2014 | 1216 | 2014 |
| Densely interconnected transcriptional circuits control cell states in human hematopoiesis N Novershtern, A Subramanian, LN Lawton, RH Mak, WN Haining, ... Cell 144 (2), 296-309, 2011 | 1147 | 2011 |
| Tissue classification with gene expression profiles A Ben-Dor, L Bruhn, N Friedman, I Nachman, M Schummer, Z Yakhini Proceedings of the fourth annual international conference on Computational …, 2000 | 1099 | 2000 |
| Context-specific independence in Bayesian networks C Boutilier, N Friedman, M Goldszmidt, D Koller arXiv preprint arXiv:1302.3562, 2013 | 1051 | 2013 |
| The Bayesian structural EM algorithm N Friedman arXiv preprint arXiv:1301.7373, 2013 | 1026 | 2013 |
| Comprehensive comparative analysis of strand-specific RNA sequencing methods JZ Levin, M Yassour, X Adiconis, C Nusbaum, DA Thompson, N Friedman, ... Nature methods 7 (9), 709-715, 2010 | 973 | 2010 |
| Learning the structure of dynamic probabilistic networks N Friedman, K Murphy, S Russell arXiv preprint arXiv:1301.7374, 2013 | 936 | 2013 |
| Learning Bayesian network structure from massive datasets: The" sparse candidate" algorithm N Friedman, I Nachman, D Pe'er arXiv preprint arXiv:1301.6696, 2013 | 924 | 2013 |
Daphne KollerCEO and Founder, insitroVerified email at insitro.com
