Nir Friedman
Nir Friedman
Professor of Computer Science and Medicine, Hebrew University
Verified email at - Homepage
Cited by
Cited by
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
Probabilistic graphical models: principles and techniques
D Koller, N Friedman
MIT press, 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
Bayesian network classifiers
N Friedman, D Geiger, M Goldszmidt
Machine learning 29, 131-163, 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
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
Inferring cellular networks using probabilistic graphical models
N Friedman
Science 303 (5659), 799, 2004
Image segmentation in video sequences: A probabilistic approach
N Friedman, S Russell
arXiv preprint arXiv:1302.1539, 2013
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
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
Learning probabilistic relational models
N Friedman, L Getoor, D Koller, A Pfeffer
IJCAI 99, 1300-1309, 1999
Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks
N Friedman, D Koller
Machine learning 50, 95-125, 2003
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
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
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
Context-specific independence in Bayesian networks
C Boutilier, N Friedman, M Goldszmidt, D Koller
arXiv preprint arXiv:1302.3562, 2013
The Bayesian structural EM algorithm
N Friedman
arXiv preprint arXiv:1301.7373, 2013
Learning the structure of dynamic probabilistic networks
N Friedman, K Murphy, S Russell
arXiv preprint arXiv:1301.7374, 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
Learning Bayesian network structure from massive datasets: The" sparse candidate" algorithm
N Friedman, I Nachman, D Pe'er
arXiv preprint arXiv:1301.6696, 2013
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