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ORCIDDana Pe'er
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- affiliation: Columbia University, New York City, USA
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2020 – today
- 2024
[c10]Doron Haviv, Russell Zhang Kunes, Thomas Dougherty, Cassandra Burdziak, Tal Nawy, Anna Gilbert, Dana Pe'er:
Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformer. ICML 2024- [i5]Doron Haviv, Russell Zhang Kunes, Thomas Dougherty, Cassandra Burdziak, Tal Nawy, Anna Gilbert, Dana Pe'er:
Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers. CoRR abs/2404.09411 (2024) - 2023
- [j8]Cassandra Burdziak, Chujun Julia Zhao
, Doron Haviv, Direna Alonso-Curbelo, Scott W. Lowe, Dana Pe'er:
scKINETICS: inference of regulatory velocity with single-cell transcriptomics data. Bioinform. 39(Supplement-1): 394-403 (2023) - [c9]Russell Z. Kunes, Mingzhang Yin, Max Land, Doron Haviv, Dana Pe'er, Simon Tavaré:
Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping. AAAI 2023: 8405-8412 - 2022
- [i4]Russell Z. Kunes, Mingzhang Yin, Max Land, Doron Haviv, Dana Pe'er, Simon Tavaré:
Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping. CoRR abs/2208.06124 (2022)
2010 – 2019
- 2019
- [j7]Sonja Nowotschin, Manu Setty, Ying-Yi Kuo, Vincent Liu, Vidur Garg, Roshan Sharma, Claire S. Simon, Nestor Saiz, Rui Gardner, Stéphane C. Boutet, Deanna M. Church, Pamela A. Hoodless, Anna-Katerina Hadjantonakis, Dana Pe'er:
The emergent landscape of the mouse gut endoderm at single-cell resolution. Nat. 569(7756): 361-367 (2019) - [i3]Cassandra Burdziak, Elham Azizi, Sandhya Prabhakaran, Dana Pe'er:
A Nonparametric Multi-view Model for Estimating Cell Type-Specific Gene Regulatory Networks. CoRR abs/1902.08138 (2019) - 2016
- [c8]Sandhya Prabhakaran, Elham Azizi, Ambrose J. Carr, Dana Pe'er:
Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data. ICML 2016: 1070-1079 - 2013
- [c7]Smita Krishnaswamy, Bernd Bodenmiller, Dana Pe'er:
Can CAD cure cancer? DAC 2013: 142:1-142:2 - [i2]Nir Friedman, Iftach Nachman, Dana Pe'er:
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm. CoRR abs/1301.6696 (2013) - 2012
- [j6]Aris Floratos, Barry Honig, Dana Pe'er, Andrea Califano:
Using systems and structure biology tools to dissect cellular phenotypes. J. Am. Medical Informatics Assoc. 19(2): 171-175 (2012) - [i1]Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman:
Learning Module Networks. CoRR abs/1212.2517 (2012) - 2010
- [j5]Felix Sanchez-Garcia, Uri David Akavia, Eyal Mozes, Dana Pe'er:
JISTIC: Identification of Significant Targets in Cancer. BMC Bioinform. 11: 189 (2010)
2000 – 2009
- 2009
- [j4]Karen Sachs, Solomon Itani, Jennifer Carlisle, Garry P. Nolan, Dana Pe'er, Douglas A. Lauffenburger:
Learning Signaling Network Structures with Sparsely Distributed Data. J. Comput. Biol. 16(2): 201-212 (2009) - 2006
- [j3]Dana Pe'er, Amos Tanay, Aviv Regev:
MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals. J. Mach. Learn. Res. 7: 167-189 (2006) - 2005
- [j2]Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman:
Learning Module Networks. J. Mach. Learn. Res. 6: 557-588 (2005) - 2004
- [c6]Karen Sachs, Omar D. Perez, Dana Pe'er, Garry P. Nolan, David K. Gifford, Tommi S. Jaakkola, Douglas A. Lauffenburger:
Analysis of Signaling Pathways in Human T-Cells Using Bayesian Network Modeling of Single Cell Data. CSB 2004: 644 - 2003
- [b1]Dana Pe'er:
From gene expression to molecular pathways (מביטוי גנים למסלולים מולקולריים.). Hebrew University of Jerusalem, Israel, 2003 - [c5]Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman:
Learning Module Networks. UAI 2003: 525-534 - 2002
- [c4]Dana Pe'er, Aviv Regev, Amos Tanay:
Minreg: Inferring an active regulator set. ISMB 2002: 258-267 - 2001
- [c3]Dana Pe'er, Aviv Regev, Gal Elidan, Nir Friedman:
Inferring subnetworks from perturbed expression profiles. ISMB (Supplement of Bioinformatics) 2001: 215-224 - 2000
- [j1]Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er:
Using Bayesian Networks to Analyze Expression Data. J. Comput. Biol. 7(3-4): 601-620 (2000) - [c2]Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er:
Using Bayesian networks to analyze expression data. RECOMB 2000: 127-135
1990 – 1999
- 1999
- [c1]Nir Friedman, Iftach Nachman, Dana Pe'er:
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm. UAI 1999: 206-215
Coauthor Index
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