Nir Friedman

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Publications

• Sorted by type
• Sorted by area

• Probabilistic Graphical Models
• Representation and Inference in Probablistic Graphical Models
• Learning Probablistic Graphical Models
• Computational Biology
• Computational Analysis of Gene Expression Data
• Reconstructing Regulatory Networks
• Epigenetics and the role of chromatin in regulation
• Genetic Interactions
• Phylogentic Inference
• Machine Learning
• Belief Change and Nonmonotonic Reasoning

Representation and Inference in Probablistic Graphical Models

2010

• Mean Field Variational Approximation for Continuous-Time Bayesian Networks with I. Cohn, T. El-Hay, and R. Kupferman.
  J. Machine Learning Research 11:2745-2738, 2010.
  PDF
 

• Continuous-Time Belief Propagation with T. El-Hay, I. Cohn, and R. Kupferman.
  Proc. Twenty Seventh International Conf. on Machine Learning (ICML 10), 2010.
  PDF
 

2009

• Mean Field Variational Approximation for Continuous-Time Bayesian Networks with I. Cohn, T. El-Hay, and R. Kupferman.
  Proc. Twenty Fifth Conf. on Uncertainty in Artificial Intelligence (UAI 09), 2009.
  PDF
 

• Probabilistic Graphical Models: Principles and Techniques with D. Koller.
  MIT Press, 2009.
  
 

• Convexifying the Bethe Free Energy with O. Meshi, A. Jaimovich, and A. Globerson.
  Proc. Twenty Fifth Conf. on Uncertainty in Artificial Intelligence (UAI 09), 2009.
  PDF
 

2008

• Gibbs Sampling in Factorized Continuous-Time Markov Processes with T. El-Hay and R. Kupferman.
  Proc. Twenty Fourth Conf. on Uncertainty in Artificial Intelligence (UAI 08), 2008.
  PDF Presentation
 

2007

• Template Based Inference in Symmetric Relational Markov Random Fields with A. Jaimovich and O. Meshi.
  The 23rd Conference on Uncertainty in Artificial Intelligence (UAI'07), 2007.
  PDF
 

2006

• Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks with R. Kupferman.
  Proc. Twenty Second Conf. on Uncertainty in Artificial Intelligence (UAI 06), 2006.
  PDF
 

2001

• Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables with T. El-Hay.
  Proc. Seventeenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2001.
  PostScript PDF Presentation
 

2000

• Likelihood Computations Using Value Abstraction with D. Geiger and N. Lotner.
  Proc. Sixteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2000.
  PostScript PDF Presentation
 

1998

• Structured Representation of Complex Stochastic Systems with D. Koller and A. Pfeffer.
  Fifteenth National Conf. on Artificial Intelligence (AAAI), 1998.
  PostScript PDF
 

1996

• Context-Specific Independence in Bayesian Networks with C. Boutilier, M. Goldszmidt, and D. Koller.
  Proc. Twelfth Conf. on Uncertainty in Artificial Intelligence (UAI), 1996.
  PostScript PDF
 

Learning Probablistic Graphical Models

2009

• Probabilistic Graphical Models: Principles and Techniques with D. Koller.
  MIT Press, 2009.
  
 

2007

• "Ideal parent" structure learning for continuous variable Bayesian networks with G. Elidan and I. Nachman.
  J. Machine Learning Research, 8:1799--1833 , 2007.
  PDF
 

2006

• Continuous Time Markov Networks with T. El-Hay, D. Koller, and R. Kupferman.
  Proc. Twenty Second Conf. on Uncertainty in Artificial Intelligence (UAI 06), 2006.
  PDF
 

• Multivariate Information Bottleneck with N. Slonim and N. Tishby.
  Neural Computation 18:1739-89, 2006.
  
 

2005

• Learning hidden variable networks: The information bottleneck approach with G. Elidan.
  J. Machine Learning Research, 6:81--127, 2005.
  PDF
 

• Learning Module Networks with E. Segal, D. Pe'er, A. Regev, and D. Koller.
  J. Machine Learning Research, 6:557--588 , 2005.
  PDF
 

2004

• Ideal parent structure learning for continuous variable networks with I. Nachman and G. Elidan.
  Proc. Twentieth Conference on Uncertainty in Artificial Intelligence, 2004.
  PostScript PDF Presentation
 

2003

• The Information Bottleneck EM Algorithm with G. Elidan.
  Proc. Nineteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2003.
  PostScript PDF Presentation
 

• Being Bayesian about Network Structure: A Bayesian Approach toStructure Discovery in Bayesian Networks with D. Koller.
  Machine Learning, 50:95-126, 2003.
  PostScript PDF
 

2002

• Data perturbation for escaping local maxima in learning with G. Elidan, M. Ninio, and D. Schuurmans.
  Eighteenth National Conf. on Artificial Intelligence (AAAI), 2002.
  PostScript PDF Presentation Web Supplement
 

• Learning probabilistic models of relational structure with L. Getoor, B. Taskar, and D. Koller.
  Journal of Machine Learning Research, 3:679-707, 2002.
  PostScript PDF
 

• Unsupervised Document Classification using Sequential Information Maximization with N. Slonim and N. Tishby.
  Proc.25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002.
  PostScript PDF
 

2001

• Learning the dimensionality of hidden variables with G. Elidan.
  Proc. Seventeenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2001.
  PostScript PDF Presentation
 

• Multivariate Information Bottleneck with O. Mosenzon, N. Slonim, and N. Tishby.
  Proc. Seventeenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2001.
  PostScript PDF Presentation
  Recipient of the Best Paper Award
 

• Learning probabilistic relational models with L. Getoor, D. Koller, and A. Pfeffer.
  S. Dvzeroski and N. Lavrac, eds. Relational Data Mining, Springer-Verlag, pp. 307-337, 2001.
  
 

• Learning probabilistic models of relational structure with L. Getoor, B. Taskar, and D. Koller.
  Eighteenth International Conference on Machine Learning, 2001.
  PostScript PDF
 

• Agglomerative Multivariate Information Bottleneck with N. Slonim and N. Tishby.
  Neural Information Processing Systems (NIPS 01), 2001.
  PostScript PDF
 

2000

• Discovering Hidden Variables: A Structure-Based Approach with G. Elidan, N. Lotner, and D. Koller.
  Neural Information Processing Systems, 2000, 2000.
  PostScript PDF Presentation
 

• Being Bayesian about Network Structure with D. Koller.
  Proc. Sixteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2000.
  PostScript PDF Presentation
 

• Gaussian Process Networks with I. Nachman.
  Proc. Sixteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2000.
  PostScript PDF Presentation
 

1999

• Discovering the Hidden Structure of Complex Dynamic Systems with X. Boyen and D. Koller.
  Proc. Fifteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 1999.
  PostScript PDF
 

• Learning Probabilistic Relational Models with L. Getoor, D. Koller, and A. Pfeffer.
  Sixteenth International Conf. on Artificial Intelligence (IJCAI), 1999.
  PostScript PDF
 

• On the Application of The Bootstrap for Computing Confidence Measureson Features of Induced Bayesian Networks with M. Goldszmidt and A. Wyner.
  Proc. Seventh International Workshop on Artificial Intelligence and Statistics, 1999.
  PostScript PDF
 

• Data Analysis with Bayesian Networks: A Bootstrap Approach with M. Goldszmidt and A. Wyner.
  Proc. Fifteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 1999., 1999.
  PostScript PDF
 

• Efficient Learning using Constrained Sufficient Statistics with L. Getoor.
  Proc. Seventh International Workshop on Artificial Intelligence and Statistics, 1999 , 1999.
  PostScript PDF
 

• Learning Bayesian network structure from massive datasets: The ``SparseCandidate'' algorithm with D. Pe'er and I. Nachman.
  Proc. Fifteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 1999.
  PostScript PDF Presentation
 

1998

• Learning the Structure of Dynamic Probabilistic Networks with K. Murphy and S. Russell.
  Fourteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 1998.
  PostScript PDF
 

• The Bayesian structural EM algorithm
  Fourteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 1998.
  PostScript PDF
 

• Learning Bayesian networks with local structure with M. Goldszmidt.
  M. I. Jordan ed. Learning and Inference in Graphical Models, 1998.
  PostScript PDF
 

• Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting with M. Goldszmidt and T. J. Lee.
  Fifteenth Inter. Conf. on Machine Learning (ICML), 1998.
  PostScript PDF
 

1997

• Challenge: Where is the Impact of Bayesian Networks in Learning? with D. Heckerman, M. Goldszmidt, and S. Russell.
  Proc. Fifteenth International Joint Conference on Artificial Intelligence (IJCAI). 1997. , 1997.
  PostScript PDF
 

• Learning belief networks in the presence of missing values and hidden variables
  Fourteenth Inter. Conf. on Machine Learning (ICML97), 1997.
  PostScript PDF
 

• Sequential update of Bayesian network structure with M. Goldszmidt.
  Proc. Thirteenth Conf. on Uncertainty in Artificial Intelligence (UAI 97), 1997.
  PostScript PDF
 

• Bayesian network classifiers with D. Geiger and M. Goldszmidt.
  Machine Learning 29:131--163, 1997.
  PostScript PDF
 

1996

• Building classifiers using bayesian networks with M. Goldszmidt.
  Thirteenth National Conf. on Artificial Intelligence (AAAI96), 1996.
  PostScript PDF
 

• Discretizing continuous attributes while learning Bayesian networks with M. Goldszmidt.
  Thirteenth Inter. Conf. on Machine Learning (ICML96), 1996.
  PostScript PDF
 

• Learning Bayesian networks with local structure with M. Goldszmidt.
  Proc. Twelfth Conf. on Uncertainty in Artificial Intelligence (UAI 96), 1996.
  PostScript PDF
 

• On the Sample Complexity of Learning Bayesian Networks with Z. Yakhini.
  Proc. Twelfth Conf. on Uncertainty in Artificial Intelligence (UAI 96), 1996.
  PostScript PDF
 

Computational Analysis of Gene Expression Data

2011

• Full-length transcriptome assembly from RNA-Seq data without a reference genome with M.G. Grabherr, B.J. Haas, M. Yassour, J.Z. Levin, D.A. Thompson, I. Amit, X. Adiconis, L. Fan, R. Raychowdhury, Q. Zeng, Z. Chen, E. Mauceli, N. Hacohen, A. Gnirke, N. Rhind, F. di Palma, B.W. Birren, C. Nusbaum, K. Toh-Lindblad, and A. Regev.
  Nature Biotechnology, 2011.
  
 

• Densely interconnected transcriptional circuits control cell states in human hematopoiesis with N. Novershtern, A. Subramanian, L.N. Lawton, R.H. Mak, W.N. Haining, M.E. McConkey, N. Habib, N. Yosef, CY. Chang, T. Shay, G.M. Frampton, A.C. Drake, I. Leskov, B. Nilsson, F. Preffer, D. Dombkowski, J.W. Evans, T. Liefeld, J.S. Smutko, J. Chen, R.A. Young, T.R. Golub, A. Regev, and B.L. Ebert.
  Cell 144(2):296-309, 2011.
  
 

• Exploring Transcription Regulation through Cell-to-cell Variability with R. Rinott and A. Jaimovich.
  Proc. Natl. Acad. Sci. USA 108:6329-34, 2011.
  
 

• An integrative clustering and modeling algorithm for dynamical gene expression data with J. Sivriver and N. Habib.
  Nineteenth Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), appears in Bioinformatics 27:i392-400, 2011.
  
 

2010

• Parentally induced transgenerational environmental reporgramming of metabolic gene expression in mammals with B.R. Carone, L. Fauquier, N. Habib, J.M. Shea, C.E. Hart, R. Li, C. Bock, H. Gu, P.D. Zamore, A. Meissner, Z. Weng, H.A. Hofmann, and O.J. Rando.
  Cell 143(7):1084-96, 2010.
  
 

• Comprehensive comparative analysis of strand-specific RNA sequencing methods with J. Z. Levin, M. Yassour, X. Adiconis, C. Nusbaum, D. A. Thompson, A. Gnirke, and A. Regev.
  Nature Methods 7(9):709-15, 2010.
  
 

• Blood mononuclear cell gene expression signature of postpartum depression with R.H. Segman, T. Goltser-Dubner, I. Weiner, L. Canetti, E. Galili-Weisstub, A. Miliwidsky, V. Pavlov, and D. Hochner-Celnikier.
  Molecular Psychiatry 15:93-100, 2010.
  
 

• Strand-specific RNA sequencing reveals extensive regulated long antisense transcripts that are conserved across yeast species with M. Yassour, J. Pfiffner, J. Z. Levin, X. Adiconis, A. Gnirke, C. Nusbaum, D. A. Thompson, and A. Regev.
  Genome Biology 11(8):R87, 2010.
  
 

2009

• Ab initio Construction of a Eukaryotic Transcriptome by Massively Parallel mRNA Sequencing with M. Yassour, T. Kaplan, H. B. Fraser, J. Z. Levin, J. Pfiffner, X. Adiconis, G. Schroth, S. Luo, I. Khrebtukova, A. Gnirke, C. Nusbaum, D. A. Thompson, and A. Regev.
  Proc. National Academy of Sciences USA, 106:3264-9, 2009.
  Web Supplement
 

2008

• Mitochondrial processes are impaired in hereditary inclusion body myopathy with I. Eisenberg, N. Novershtern, Z. Itzhaki, M. Becker-Cohen, M. Sadeh, P. H. Willems, W. J. Koopman, and S. Mitrani-Rosenbaum.
  Hum. Mol. Genet., 17(22):3663-74, 2008.
  
 

• Physiological and molecular evidence of heat acclimation memory: A lesson from thermal responses and ischemic cross-tolerance in the heart with A. Tetievsky, O. Cohen, L. Eli-Berchoer, G. Gerstenblith, MD. Stern, I. Wapinski, and M. Horowitz.
  Physiological Genomics, 2008.
  
 

2007

• A Functional and Regulatory Map of Asthma with N. Novershtern, Z. Itzhaki, O. Manor, and N. Kaminski.
  American Journal of Respiratory and Cell Molecular Biology 38(3):324-336 , 2007.
  Web Supplement
 

2005

• From signatures to models: Understanding cancer using microarrays with E. Segal, N. Kaminski, A. Regev, and D. Koller.
  Nature Genetics, 37 Suppl:S38-45, 2005.
  PDF
 

• Peripheral blood mononuclear cell gene expression profiles identify emergent post-traumatic stress disorder among trauma survivors with R.H. Segman, N. Shefi, T. Goltser-Dubner, N. Kaminski, and A.Y. Shalev.
  Molecular Psychiatry 10:500-13, 2005.
  PDF
 

2004

• Blood transcriptional signatures of multiple sclerosis: Unique gene expression of disease activity with A. Achiron, M. Gurevich, N. Kaminski, and M. Mandel.
  Annals of Neurology, 55:410-7, 2004.
  
 

• Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays with Y. Barash, E. Dehan, M. Krupsky, W. Franklin, M. Geraci, and N. Kaminski.
  Bioinformatics, 20:839-4, 2004.
  
 

• Stress-related genomic responses during the course of heat acclimation and its association with ischemic-reperfusion cross-tolerance with M. Horowitz, L. Eli-Berchoer, I. Wapinski, and E. Kodesh.
  Journal of Applied Physiology, 97:1496-507, 2004.
  
 

• A module map showing conditional activity of expression modules in cancer with E. Segal, D. Koller, and A. Regev.
  Nature Genetics, 36:1090-8, 2004.
  Web Supplement
 

• GeneXPress: A visualization and statistical analysis tool for gene expression and sequence data with E. Segal, R. Yelensky, A. Kaushal, T. Pham, A. Regev, and D. Koller.
  Eleventh Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), 2004.
  PDF Presentation
 

2003

• Human and porcine early kidney precursors as a new source for transplantation with B. Dekel, T. Burakova, F.D. Arditti, S. Reich-Zeliger, O. Milstein, S. Aviel-Ronen, G. Rechavi, N. Kaminski, J.H. Passwell, and Y. Reisner.
  Nature Medicine 9:53-60, 2003.
  
 

2002

• Context-Specific Bayesian Clustering for Gene Expression Data with Y. Barash.
  Journal of Computational Biology 9:169-191, 2002.
  PostScript PDF
 

• Overabundance Analysis and Class Discovery in Gene Expression Data with A. Ben-Dor and Z. Yakhini.
  2002.
  PostScript PDF
 

• Statistical methods for analyzing gene expression data for cancer research with N. Kaminski.
  H. W. Mewes, H. Seidel, and B. Weiss, eds. Ernest Schering Foundation Workshop: Volume 38: Bioinformatics and Genome Analysis, 2002.
  
 

• Practical approaches to analyzing results of microarray experiments with N. Kaminski.
  American Journal of Respiratory and Cell Molecular Biology, 27:125-132, 2002.
  PDF
 

2001

• Context-Specific Bayesian Clustering for Gene Expression Data with Y. Barash.
  Proc. Fifth Annual Inter. Conf. on Computational Molecular Biology (RECOMB), 2001.
  PostScript PDF Presentation
 

• Class Discovery in Gene Expression Data with A. Ben-Dor and Z. Yakhini.
  Proc. Fifth Annual Inter. Conf. on Computational Molecular Biology (RECOMB), 2001.
  PostScript PDF
 

• Rich Probabilistic Models for Gene Expression with E. Segal, B. Taskar, A. Gasch, and D. Koller.
  9th Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), 2001. Appeared as Bioinformatics, 17, suppl. 1: S243-52, 2001.
  PostScript PDF Presentation Web Supplement
 

2000

• Tissue Classification with Gene Expression Profiles with A. Ben-Dor, L. Bruhn, I. Nachman, M. Schummer, and Z. Yakhini.
  J. Computational Biology, 7: 559-584, 2000.
  PostScript PDF
 

• Tissue Classification with Gene Expression Profiles with A. Ben-Dor, L. Bruhn, I. Nachman, M. Schummer, and Z. Yakhini.
  Proc. Fourth Annual Inter. Conf. on Computational Molecular Biology (RECOMB), 2000.
  PostScript PDF
 

Reconstructing Regulatory Networks

2011

• Physical Module Networks: an integrative approach for reconstructing transcription regulation with N. Novershtern and A. Regev.
  Nineteenth Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), appears in Bioinformatics 27:i177-185, 2011.
  
 

• Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells with M. Rabani, JZ. Levin, L. Fan, X. Adiconis, R. Raychowdhury, M. Garber, A. Gnirke, C. Nusbaum, N. Hacohen, I. Amit, and A. Regev.
  Nature Biotechnology 29:436-42, 2011.
  
 

• High Throughput Determination of TGFbeta1/SMAD3 Targets in A549 Lung Epithelial Cells with Y. Zhang, D. Handley, T. Kaplan, H. Yu, A.S. Bais, T. Richards, K.V. Pandit, Q. Zeng, P.V. Benos, O. Eickelberg, and N. Kaminski.
  PLoS One 6:e20319, 2011.
  
 

2008

• Structure and function of a transcriptional network activated by the MAPK Hog1 with A. P. Capaldi, T. Kaplan, Y. Liu, N. Habib, A. Regev, and E. K. O'Shea.
  Nature Genetics, 40:1300-1306, 2008.
  PDF Web Supplement
 

• A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval with N. Habib, T. Kaplan, and H. Margalit.
  PLoS Comput Biol, 4(2):e1000010, 2008.
  PDF Web Supplement
 

2006

• Towards an integrated protein-protein interaction network: a relational Markov network approach with A. Jaimovich, G. Elidan, and H. Margalit.
  J. Computational Biology 13:145-164, 2006.
  PDF
 

2005

• Towards an Integrated Protein-protein Interaction Network with A. Jaimovich, G. Elidan, and H. Margalit.
  Ninth Annual International Conference on Computational Molecular Biology, 2005.
  PDF
 

• Ab initio prediction of transcription factor targets using structural knowledge with T. Kaplan and H. Margalit.
  PLoS Comput Biol1(1):e1, 2005.
  Web Supplement
 

• Predicting transcription factor binding sites using structural knowledge with T. Kaplan and H. Margalit.
  Ninth Annual International Conference on Computational Molecular Biology, 2005.
  
 

• From signatures to models: Understanding cancer using microarrays with E. Segal, N. Kaminski, A. Regev, and D. Koller.
  Nature Genetics, 37 Suppl:S38-45, 2005.
  PDF
 

2004

• CIS: Compound importance sampling method for protein-DNA binding site p-value estimation with Y. Barash, G. Elidan, and T. Kaplan.
  Bioinformatics, 2004.
  Presentation Web Supplement
 

• Inferring cellular networks using probabilistic graphical models
  Science. 303:799-805, 2004.
  
 

• Sfp1 is a stress- and nutrient-sensitive regulator of ribosomal protein gene expression with R.M. Marion, A. Regev, E. Segal, Y. Barash, D. Koller, and E.K. O'Shea.
  Proc. National Academy of Sciences USA, 101:14315-22, 2004.
  
 

• Inferring quantitative models of regulatory networks from expression data with I. Nachman and A. Regev.
  Eleventh Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), Appeared in Bioinformatics. 20 Suppl. 1:I248-I256, 2004.
  PDF Presentation
 

• Identifying regulatory networks: Lessons from yeast to humans with A. Regev.
  American Journal of Respiratory and Cell Molecular Biology, 31:125-132, 2004.
  PDF
 

2003

• Modeling Dependencies in Protein-DNA Binding Sites with Y. Barash, G. Elidan, and T. Kaplan.
  Proc. Seventh Annual Inter. Conf. on Computational Molecular Biology (RECOMB), 2003.
  PostScript PDF Presentation
 

• Learning Module Networks with E. Segal, D. Pe'er, A. Regev, and D. Koller.
  Proc. Nineteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2003.
  PostScript PDF Presentation
 

• Module Networks: identifying regulatory modules and their condition- specific regulators from gene expression data with E. Segal, M. Shapira, A. Regev, D. Pe'er, D. Botstein, and D. Koller.
  Nature Genetics, 34:166-178, 2003.
  Web Supplement
 

2002

• Context-Specific Bayesian Clustering for Gene Expression Data with Y. Barash.
  Journal of Computational Biology 9:169-191, 2002.
  PostScript PDF
 

• From Promoter Sequence to Expression: A Probabilistic Framework with E. Segal, Y. Barash, I. Simon, and D. Koller.
  Proc. Sixth Annual Inter. Conf. on Computational Molecular Biology (RECOMB), 2002.
  PostScript PDF Presentation
 

2001

• Context-Specific Bayesian Clustering for Gene Expression Data with Y. Barash.
  Proc. Fifth Annual Inter. Conf. on Computational Molecular Biology (RECOMB), 2001.
  PostScript PDF Presentation
 

• Inferring Subnetworks from Perturbed Expression Profiles with D. Pe'er, A. Regev, and G. Elidan.
  9th Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), 2001. Appeared in Bioinformatics 17, suppl. 1 S215-24, 2001.
  PostScript PDF Presentation Web Supplement
  Recipient of the Best Paper Award
 

2000

• Using Bayesian Network to Analyze Expression Data with M. Linial, I. Nachman, and D. Pe'er.
  J. Computational Biology 7:601-620, 2000.
  PostScript PDF
 

• Using Bayesian Network to Analyze Expression Data with M. Linial, I. Nachman, and D. Pe'er.
  Proc. Fourth Annual Inter. Conf. on Computational Molecular Biology (RECOMB 2000), 2000.
  PostScript PDF Presentation
 

Epigenetics and the role of chromatin in regulation

2011

• Substantial Histone Reduction Modulates Genomewide Nucleosomal Occupancy and Global Transcriptional Output with B. Celona, A. Weiner, F. Di Felice, F. Mancuso, E. Cesarini, R. Rossi, L. Gregory, D. Baban, G. Rossetti, P. Grianti, M. Pagani, T. Bonaldi, J. Ragoussis, G. Camilloni, M. Bianchi, and A. Agresti.
  PLoS Biology 9:e1001086, 2011.
  
 

• Dynamics of Sir3 spreading in budding yeast: secondary recruitment sites and euchromatic localization with M. Radman-Livaja, G. Ruben, A. Weiner, R. Kamakaka, and O.J. Rando.
  EMBO J30:1012-26, 2011.
  
 

• Patterns and mechanisms of ancestral histone protein inheritance in budding yeast with M. Radman-Livaja, K.F. Verzijlbergen, A. Weiner, T. van Welsem, O.J. Rando, and F. van Leeuwen.
  PLoS Biology 9:e1001075, 2011.
  
 

• Exploring Transcription Regulation through Cell-to-cell Variability with R. Rinott and A. Jaimovich.
  Proc. Natl. Acad. Sci. USA 108:6329-34, 2011.
  
 

2010

• Modularity and Directionality in Genetic Interaction Maps with A. Jaimovich, R. Rinott, M. Schuldiner, and H. Margalit.
  Eighteenth Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), appears in Bioinformatics 26, 2010.
  Web Supplement
 

• RNA polymerase mapping during stress responses reveals widespread nonproductive transcription in yeast with T.S. Kim, C.-L. Liu, M. Yassour, J. Holik, S. Buratowski, and O.J. Rando.
  Genome Biology 11(7):R75, 2010.
  
 

• Replication and Active Demethylation Represent Partially Overlapping Mechanisms for Erasure of H3K4me3 in Budding Yeast with M. Radman-Livaja, C.-L. Liu, S.L. Schreiber, and O.J. Rando.
  PLoS Genetics6:e1000837, 2010.
  
 

• High-resolution nucleosome mapping reveals transcription-dependent promoter packaging with A. Weiner, A. Hughes, M. Yassour, and O.J. Rando.
  Genome Research 20:90-100, 2010.
  Web Supplement
 

2008

• Cell Cycle- and Chaperone-Mediated Regulation of H3K56ac Incorporation in Yeast with T. Kaplan, C.-L. Liu, J. A. Erkmann, J. Holik, M. Grunstein, P. D. Kaufman, and O.J. Rando.
  PLoS Genetics, 4(11):e1000270, 2008.
  PDF
 

• Nucleosome Positioning from Tiling Microarray Data with M. Yassour, T. Kaplan, and A. Jaimovich.
  Sixteenth Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), appears in Bioinformatics 24:i139-146, 2008, 2008.
  
 

2007

• Dynamics of Replication-Independent Histone Turnover in Budding Yeast with M.F. Dion, T. Kaplan, M. Kim, S. Buratowski, and O.J. Rando.
  Science 315(5817):1405-8, 2007.
  
 

2005

• Single-nucleosome mapping of histone modifications in S. cerevisiae with C.-L. Liu, T. Kaplan, M. Kim, S. Buratowski, S.L. Schreiber, and O.J. Rando.
  PLoS Biol 3(10):e328, 2005.
  
 

Genetic Interactions

• The DNA damage road map (Perspective) with M. Schuldiner.
  Science 330:1327-8, 2010.
  
 

• From large-scale assays to mechanistic insights: computational analysis of interactions with A. Jaimovich.
  Current Opinions in Biotechnology, 2010.
  
 

• Modularity and Directionality in Genetic Interaction Maps with A. Jaimovich, R. Rinott, M. Schuldiner, and H. Margalit.
  Eighteenth Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), appears in Bioinformatics 26, 2010.
  Web Supplement
 

Phylogentic Inference

2011

• Comparative functional genomics of the fission yeasts with N. Rhind, Z. Chen, M. Yassour, DA. Thompson, BJ. Haas, N. Habib, I. Wapinski, S. Roy, MF. Lin, DI. Heiman, SK. Young, K. Furuya, Y. Guo, A. Pidoux, HM. Chen, B. Robbertse, JM. Goldberg, K. Aoki, EH. Bayne, AM. Berlin, CA. Desjardins, E. Dobbs, L. Dukaj, L. Fan, MG. Fitzgerald, C. French, S. Gujja, K. Hansen, D. Keifenheim, JZ. Levin, RA. Mosher, CA. Muller, J. Pfiffner, M. Priest, C. Russ, A. Smialowska, P. Swoboda, SM. Sykes, M. Vaughn, S. Vengrova, R. Yoder, Q. Zeng, R. Allshire, D. Baulcombe, BW. Birren, W. Brown, K. Ekwall, M. Kellis, J. Leatherwood, H. Levin, H. Margalit, R. Martienssen, CA. Nieduszynski, JW. Spatafora, JZ. Dalgaard, P. Baumann, H. Niki, A. Regev, and C. Nusbaum.
  Science, 2011.
  
 

2009

• Identifying novel constrained elements by exploiting biased substitution patterns with M. Gerber, M. Guttman, M. Clamp, M.C. Zody, and X. Xie.
  Seventeenth Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), appears in Bioinformatics 25:i54-i62, 2009.
  
 

2007

• Phylogeny Reconstruction: Increasing the Accuracy of Pairwise Distance Estimation Using Bayesian Inference of Evolutionary Rates with M. Ninio, E. Privman, and T. Pupko.
  European Conference on Computational Biology. Appeared in Bioinformatics 23(2):e136-41, 2007.
  PDF
 

• Automatic Genome-Wide Reconstruction of Phylogenetic Gene Trees with I. Wapinski, A. Pfeffer, and A. Regev.
  Fifteenth Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), appeared in Bioinformatics 23:i549-58, 2007.
  PDF
 

• Natural history and evolutionary principles of gene duplication in fungi with I. Wapinski, A. Pfeffer, and A. Regev.
  Nature 449:54-61, 2007.
  Web Supplement
 

2005

• A Gamma mixture model better accounts for among site rate heterogeneity with I. Mayrose and T. Pupko.
  European Conference on Computational Biology. Appeared in Bioinformatics 21 Suppl 2:ii151-ii158, 2005.
  PDF
 

2002

• A Structural EM Algorithm for Phylogenetic Inference with M. Ninio, I. Pe'er, and T. Pupko.
  Journal of Computational Biology, 9:331-353, 2002.
  PostScript PDF Presentation
 

• A branch-and-bound algorithm for the inference of ancestral amino-acid sequences when the replacement rate varies among sites with T. Pupko, I. Pe'er, M. Hasegawa, and D. Graur.
  Bioinformatics, 18:1116-1123, 2002.
  PDF Web Supplement
 

2001

• A Structural EM Algorithm for Phylogenetic Inference with M. Ninio, I. Pe'er, and T. Pupko.
  Proc. Fifth Annual Inter. Conf. on Computational Molecular Biology (RECOMB 2001), 2001.
  PostScript PDF Presentation
 

Machine Learning

2010

• Mean Field Variational Approximation for Continuous-Time Bayesian Networks with I. Cohn, T. El-Hay, and R. Kupferman.
  J. Machine Learning Research 11:2745-2738, 2010.
  PDF
 

• Continuous-Time Belief Propagation with T. El-Hay, I. Cohn, and R. Kupferman.
  Proc. Twenty Seventh International Conf. on Machine Learning (ICML 10), 2010.
  PDF
 

2009

• Mean Field Variational Approximation for Continuous-Time Bayesian Networks with I. Cohn, T. El-Hay, and R. Kupferman.
  Proc. Twenty Fifth Conf. on Uncertainty in Artificial Intelligence (UAI 09), 2009.
  PDF
 

• Convexifying the Bethe Free Energy with O. Meshi, A. Jaimovich, and A. Globerson.
  Proc. Twenty Fifth Conf. on Uncertainty in Artificial Intelligence (UAI 09), 2009.
  PDF
 

2008

• Gibbs Sampling in Factorized Continuous-Time Markov Processes with T. El-Hay and R. Kupferman.
  Proc. Twenty Fourth Conf. on Uncertainty in Artificial Intelligence (UAI 08), 2008.
  PDF Presentation
 

2006

• Continuous Time Markov Networks with T. El-Hay, D. Koller, and R. Kupferman.
  Proc. Twenty Second Conf. on Uncertainty in Artificial Intelligence (UAI 06), 2006.
  PDF
 

• Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks with R. Kupferman.
  Proc. Twenty Second Conf. on Uncertainty in Artificial Intelligence (UAI 06), 2006.
  PDF
 

• Multivariate Information Bottleneck with N. Slonim and N. Tishby.
  Neural Computation 18:1739-89, 2006.
  
 

2004

• Efficient exact p-value computation for small sample, sparse, and surprising categorical data with G. Bejerano and N. Tishby.
  J Computational Biology 11:867-886, 2004.
  PDF
 

2002

• Data perturbation for escaping local maxima in learning with G. Elidan, M. Ninio, and D. Schuurmans.
  Eighteenth National Conf. on Artificial Intelligence (AAAI), 2002.
  PostScript PDF Presentation Web Supplement
 

• Robust temporal and spectral modeling for query by melody with S. Shwartz, S. Dobnov, and Y. Singer.
  Proc.25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002.
  
 

2001

• Multivariate Information Bottleneck with O. Mosenzon, N. Slonim, and N. Tishby.
  Proc. Seventeenth Conf. on Uncertainty in Artificial Intelligence (UAI), 2001.
  PostScript PDF Presentation
  Recipient of the Best Paper Award
 

1999

• Model-Based Bayesian Exploration with R. Dearden and D. Andre.
  Proc. Fifteenth Conf. on Uncertainty in Artificial Intelligence (UAI), 1999.
  PostScript PDF
 

1998

• Bayesian Q-Learning with R. Dearden and S. Russell.
  Fifteenth National Conf. on Artificial Intelligence (AAAI), 1998.
  PostScript PDF
 

• Efficient Bayesian parameter estimation in large discrete domains with Y. Singer.
  Neural Information Processing Systems (NIPS 98), 1998., 1998.
  PostScript PDF
 

• Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting with M. Goldszmidt and T. J. Lee.
  Fifteenth Inter. Conf. on Machine Learning (ICML), 1998.
  PostScript PDF
 

1997

• Generalized Prioritized Sweeping with D. Andre and R. Parr.
  Neural Information Processing Systems., 1997.
  PostScript PDF
 

• Bayesian network classifiers with D. Geiger and M. Goldszmidt.
  Machine Learning 29:131--163, 1997.
  PostScript PDF
 

• Image Segmentation in Video Sequences: A Probabilistic Approach with S. Russell.
  Proc. Thirteenth Conf. on Uncertainty in Artificial Intelligence (UAI 97), 1997.
  PostScript PDF
 

1996

• Building classifiers using bayesian networks with M. Goldszmidt.
  Thirteenth National Conf. on Artificial Intelligence (AAAI96), 1996.
  PostScript PDF
 

Belief Change and Nonmonotonic Reasoning

2001

• On decision-theoretic foundations for defaults with R.I. Brafman.
  Artificial Intelligence, 133:1-33, 2001, 2001.
  
 

• Plausibility measures and default reasoning with J.Y. Halpern.
  Journal of the ACM, 48:648--685, -- --2001, 2001.
  PostScript PDF
 

2000

• Conditional First-Order Logic Revisited with J.Y. Halpern and D. Koller.
  ACM Trans. Computational Logic, 1:175-207, 2000.
  PostScript PDF
 

1999

• Modeling beliefs in dynamic systesm. Part II: revision and update with J.Y. Halpern.
  Journal of AI Research 10:117-167, 1999.
  PostScript PDF
 

• Belief revision: A critique with J.Y. Halpern.
  Journal of Logic, Language, and Information 8:401-420, 1999.
  PostScript PDF
 

1998

• Belief Revision with Unreliable Observations with C. Boutilier and J.Y. Halpern.
  Fifteenth National Conf. on Artificial Intelligence (AAAI), 1998.
  PostScript PDF
 

1997

• Modeling Beliefs in Dynamic Systems
  Phd Thesis, Stanford University, 1997.
  PostScript
 

• Modeling beliefs in dynamic systems. Part I: Foundations with J.Y. Halpern.
  Artificial Intelligence, 95:257--316, 1997.
  PostScript PDF
 

1996

• Plausibility measures and default reasoning with J.Y. Halpern.
  Thirteenth National Conf. on Artificial Intelligence (AAAI96), 1996.
  PostScript PDF
 

• A qualitative Markov assumption and its implications for belief change with J.Y. Halpern.
  Proc. Twelfth Conf. on Uncertainty in Artificial Intelligence (UAI 96), 1996.
  PostScript PDF
 

• Belief revision: A critique with J.Y. Halpern.
  Fifth Inter. Conf. on Knowledge Representation and Reasoning (KR96), 1996.
  PostScript PDF
 

• Conditional first-order logic revisited with J.Y. Halpern and D. Koller.
  Thirteenth National Conf. on Artificial Intelligence (AAAI96), 1996.
  PostScript PDF
 

1995

• On Decision-Theoretic Foundations for Defaults with R.I. Brafman.
  Fourteenth International Conf. on Artificial Intelligence (IJCAI 95), 1995.
  PostScript PDF
 

• Nondeterministic Actions and the Frame Problem with C. Boutilier.
  Preliminary version appeared in Extending Theories of Actions, AAAI Spring Symposium., 1995.
  PostScript PDF
 

• Plausibility Measures: A User's Guide with J.Y. Halpern.
  Proc. Eleventh Conf. on Uncertainty in Artificial Intelligence (UAI 95), 1995.
  PostScript PDF
 

1994

• Conditional Logics of Belief Change with J.Y. Halpern.
  Proc. National Conference on Artificial Intelligence (AAAI '94), 915-921, 1994.
  PostScript PDF
 

• A Knowledge-Based framework for belief change. Part II: Revision and update with J.Y. Halpern.
  Principles of Knowledge Representation and Reasoning: Proc. Fourth International Conference (KR'94), 190-201, 1994.
  PostScript PDF
 

• On the Complexity of Conditional Logics with J.Y. Halpern.
  Principles of Knowledge Representation and Reasoning: Proc. Fourth International Conference (KR'94), 202-213., 1994.
  PostScript PDF
 

• A Knowledge-based framework for belief change. Part I: Foundations with J.Y. Halpern.
  Theoretical Aspects of Reasoning about Knowledge: Proc. Fifth Conference (TARK'94), 44-64, 1994.
  PostScript PDF
 

1992

• On the power and complexity of knowledge-charts
  Master's Thesis, Weizmann Institute of Science, 1992.