N. Friedman

发表

Bayesian Q-Learning

Stuart J. Russell, Nir Friedman, Richard Dearden, 1998, AAAI/IAAI.

Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

N. Friedman, M. Grabherr, B. Haas, 2011, Nature Biotechnology.

Plausibility Measures: A User's Guide pdf

Joseph Y. Halpern, Nir Friedman, N. Friedman, 1995, UAI.

Probabilistic Graphical Models - Principles and Techniques

Nir Friedman, Daphne Koller, D. Koller, 2009 .

Context-Specific Independence in Bayesian Networks pdf

Craig Boutilier, Nir Friedman, Daphne Koller, 1996, UAI.

Generalized Prioritized Sweeping

David Andre, Nir Friedman, Ronald Parr, 1997, NIPS.

Universal critical dynamics in high resolution neuronal avalanche data.

John M. Beggs, Braden A. W. Brinkman, N. Friedman, 2012, Physical review letters.

Model based Bayesian Exploration pdf

David Andre, Nir Friedman, Richard Dearden, 1999, UAI.

Learning the Structure of Dynamic Probabilistic Networks pdf

Kevin P. Murphy, Stuart J. Russell, Nir Friedman, 1998, UAI.

Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data

D. Pe’er, D. Koller, N. Friedman, 2003, Nature Genetics.

Inferring Cellular Networks Using Probabilistic Graphical Models

Nir Friedman, N. Friedman, 2004, Science.

Discretizing Continuous Attributes While Learning Bayesian Networks

Nir Friedman, Moisés Goldszmidt, M. Goldszmidt, 1996, ICML.

PCluster : Probabilistic Agglomerative Clustering of Gene Expression Profiles

N. Friedman, 2003 .

Comprehensive comparative analysis of strand-specific RNA sequencing methods

N. Friedman, M. Yassour, J. Levin, 2010, Nature Methods.

On the Sample Complexity of Learning Bayesian Networks

Nir Friedman, Zohar Yakhini, N. Friedman, 1996, UAI.

Learning Bayesian Networks with Local Structure pdf

Nir Friedman, Moisés Goldszmidt, M. Goldszmidt, 1996, UAI.

Efficient Bayesian Parameter Estimation in Large Discrete Domains

Yoram Singer, Nir Friedman, Y. Singer, 1998, NIPS.

Learning Belief Networks in the Presence of Missing Values and Hidden Variables

Nir Friedman, N. Friedman, 1997, ICML.

The Bayesian Structural EM Algorithm

Nir Friedman, N. Friedman, 1998, UAI.

Wishbone identifies bifurcating developmental trajectories from single-cell data

Sean C. Bendall, D. Pe’er, Michelle D. Tadmor, 2016, Nature Biotechnology.

Multivariate Information Bottleneck

Naftali Tishby, Nir Friedman, Noam Slonim, 2001, Neural Computation.

Uncertainty in artificial intelligence : proceedings of the nineteenth conference (2003), August 7-10, 2003, Acapulco, Mexico

Adnan Darwiche, N. Friedman, Christopher Meek, 2003 .

GeneXPress : A Visualization and Statistical Analysis Tool for Gene Expression and Sequence Data

D. Koller, N. Friedman, A. Regev, .

A module map showing conditional activity of expression modules in cancer

D. Koller, N. Friedman, A. Regev, 2004, Nature Genetics.

Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing

Hunter B. Fraser, N. Friedman, M. Yassour, 2009, Proceedings of the National Academy of Sciences.

Using Bayesian Networks to Analyze Expression Data

Michal Linial, Nir Friedman, Iftach Nachman, 2000, J. Comput. Biol..

Bayesian Network Classifiers

Nir Friedman, Dan Geiger, Moisés Goldszmidt, 1997, Machine Learning.

Tissue Classi cation with Gene Expression Pro les

N. Friedman, L. Bruhn, Z. Yakhini, 2004 .

Inferring subnetworks from perturbed expression profiles

Nir Friedman, Aviv Regev, Gal Elidan, 2001, ISMB.

Being Bayesian about Network Structure

Nir Friedman, Daphne Koller, D. Koller, 2000, UAI.

The Information Bottleneck EM Algorithm pdf

Nir Friedman, Gal Elidan, N. Friedman, 2002, UAI.

Learning Probabilistic Models of Relational Structure

Ben Taskar, Lise Getoor, Nir Friedman, 2001, ICML.

Sequential Update of Bayesian Network Structure pdf

Nir Friedman, Moisés Goldszmidt, M. Goldszmidt, 1997, UAI.

5 Probabilistic Relational Models

Lise Getoor, Avi Pfeffer, Nir Friedman, 2008 .

De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

Colin N. Dewey, Philip D. Blood, David A. Eccles, 2013, Nature Protocols.

Unsupervised document classification using sequential information maximization

Naftali Tishby, Nir Friedman, Noam Slonim, 2002, SIGIR '02.

Rich probabilistic models for gene expression

Ben Taskar, Nir Friedman, Daphne Koller, 2001, ISMB.

Continuous Time Markov Networks pdf

Nir Friedman, Daphne Koller, Raz Kupferman, 2006, UAI.

Design principles of cell circuits with paradoxical components

Yaron E. Antebi, N. Friedman, U. Alon, 2012, Proceedings of the National Academy of Sciences.

Single cell RNA Seq reveals dynamic paracrine control of cellular variation

Rona S. Gertner, N. Friedman, R. Raychowdhury, 2014, Nature.

2 Graphical Models in a Nutshell

D. Koller, N. Friedman, L. Getoor, 2008 .

Modeling dependencies in protein-DNA binding sites

Nir Friedman, Gal Elidan, Yoseph Barash, 2003, RECOMB '03.

T-cell receptor repertoires share a restricted set of public and abundant CDR3 sequences that are associated with self-related immunity

N. Friedman, S. Reich-Zeliger, I. Cohen, 2014, Genome research.

T cell receptor repertoires of mice and humans are clustered in similarity networks around conserved public CDR3 sequences

N. Friedman, S. Reich-Zeliger, I. Cohen, 2017, eLife.

Specificity, Privacy, and Degeneracy in the CD4 T Cell Receptor Repertoire Following Immunization

J. Shawe-Taylor, N. Friedman, S. Reich-Zeliger, 2017, Front. Immunol..

Image Segmentation in Video Sequences: A Probabilistic Approach

Stuart J. Russell, Nir Friedman, N. Friedman, 1997, UAI.

Challenge: What is the Impact of Bayesian Networks on Learning?

David Heckerman, Stuart J. Russell, Nir Friedman, 1997, IJCAI.

Towards an Integrated Protein-Protein Interaction Network

Nir Friedman, Hanah Margalit, Gal Elidan, 2005, RECOMB.

Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks

Nir Friedman, Daphne Koller, D. Koller, 2004, Machine Learning.

Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning

Nir Friedman, Daphne Koller, D. Koller, 2009 .

Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens

Thomas M. Norman, N. Friedman, R. Raychowdhury, 2016, Cell.

Chromatin state dynamics during blood formation

N. Friedman, I. Amit, Diego Jaitin, 2014, Science.

Natural history and evolutionary principles of gene duplication in fungi

N. Friedman, A. Regev, A. Pfeffer, 2007, Nature.

A Knowledge-Based Framework for Belief change, Part I: Foundations

Joseph Y. Halpern, Nir Friedman, N. Friedman, 1994, TARK.

A Structural EM Algorithm for Phylogenetic Inference

Tal Pupko, Nir Friedman, Itsik Pe'er, 2002, J. Comput. Biol..

Probabilistic Graphical Models

Nir Friedman, Daphne Koller, D. Koller, 2009, Data-Driven Computational Neuroscience.

Module Networks : Discovering Regulatory Modules and their Condition Specific Regulators from Gene Expression Data

D. Pe’er, D. Koller, N. Friedman, 2003 .

First-Order Conditional Logic Revisited

Joseph Y. Halpern, Nir Friedman, Daphne Koller, 1996, AAAI/IAAI, Vol. 2.

Learning Bayesian Networks from Data

Nir Friedman, Daphne Koller, D. Koller, 2001 .

On the Complexity of Conditional Logics

Joseph Y. Halpern, Nir Friedman, N. Friedman, 1994, KR.

Nucleosome positioning from tiling microarray data

Nir Friedman, Tommy Kaplan, Ariel Jaimovich, 2008, ISMB.

Sfp1 is a stress- and nutrient-sensitive regulator of ribosomal protein gene expression.

D. Koller, N. Friedman, A. Regev, 2004, Proceedings of the National Academy of Sciences of the United States of America.

Structure and function of a transcriptional network activated by the MAPK Hog1

N. Friedman, A. Regev, E. O’Shea, 2008, Nature Genetics.

Identifying novel constrained elements by exploiting biased substitution patterns

Xiaohui Xie, Nir Friedman, Michael C. Zody, 2009, Bioinform..

Epigenomics and the structure of the living genome

Nir Friedman, N. Friedman, O. Rando, 2015, Genome research.

Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

Nir Friedman, Aviv Regev, Carl G. de Boer, 2019, Nature Biotechnology.

Deciphering eukaryotic cis-regulatory logic with 100 million random promoters

Carl G. de Boer, N. Friedman, A. Regev, 2017, bioRxiv.

Stochastic protein expression in individual cells at the single molecule level

N. Friedman, X. Xie, L. Cai, 2006, Nature.

Linking stochastic dynamics to population distribution: an analytical framework of gene expression.

Nir Friedman, Long Cai, N. Friedman, 2006, Physical review letters.

Genetic screen of the yeast environmental stress response dynamics uncovers distinct regulatory phases

N. Friedman, Jenia Gutin, D. Joseph-Strauss, 2019, Molecular systems biology.

From signatures to models: understanding cancer using microarrays

D. Koller, N. Friedman, A. Regev, 2005, Nature Genetics.

Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis

N. Friedman, A. Regev, R. Young, 2011, Cell.

A functional and regulatory map of asthma.

Nir Friedman, Zohar Itzhaki, Naftali Kaminski, 2008, American journal of respiratory cell and molecular biology.

A high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals.

Nir Friedman, Zohar Itzhaki, Aviv Regev, 2012, Molecular cell.

High-Resolution Chromatin Dynamics during a Yeast Stress Response

Nir Friedman, Oliver J. Rando, Ido Amit, 2015, Molecular cell.

Paternally Induced Transgenerational Environmental Reprogramming of Metabolic Gene Expression in Mammals

Zhiping Weng, Nir Friedman, Ruowang Li, 2010, Cell.

Dynamics of Replication-Independent Histone Turnover in Budding Yeast

Nir Friedman, Minkyu Kim, Tommy Kaplan, 2007, Science.

Single-Nucleosome Mapping of Histone Modifications in S. cerevisiae

N. Friedman, S. Schreiber, C. Liu, 2005, PLoS biology.

Comparative Functional Genomics of the Fission Yeasts

Manolis Kellis, Nir Friedman, Hanah Margalit, 2011, Science.

Mapping Nucleosome Resolution Chromosome Folding in Yeast by Micro-C

Nir Friedman, Bryan Lajoie, Oliver J. Rando, 2015, Cell.

Dynamic and static maintenance of epigenetic memory in pluripotent and somatic cells

N. Friedman, A. Tanay, Zohar Shipony, 2014, Nature.

Patterns and Mechanisms of Ancestral Histone Protein Inheritance in Budding Yeast

Nir Friedman, Oliver J. Rando, Tibor van Welsem, 2011, PLoS biology.

High-resolution nucleosome mapping reveals transcription-dependent promoter packaging.

Nir Friedman, Moran Yassour, N. Friedman, 2010, Genome research.

High-Resolution View of the Yeast Meiotic Program Revealed by Ribosome Profiling

Nicholas T. Ingolia, N. Friedman, M. Yassour, 2011, Science.

Modeling Belief in Dynamic Systems, Part I: Foundations

Joseph Y. Halpern, Nir Friedman, N. Friedman, 1997, Artif. Intell..

Modeling beliefs in dynamic systems

N. Friedman, 1997 .

High-Resolution Sequencing and Modeling Identifies Distinct Dynamic RNA Regulatory Strategies

N. Friedman, I. Amit, R. Raychowdhury, 2014, Cell.

Trinity : reconstructing a full-length transcriptome without a genome from RNA-Seq data

N. Friedman, M. Grabherr, B. Haas, 2016 .

C 5 . 1 . 5 Bayesian Classi cation

N. Friedman, Ron Kohavi, 1999 .

Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells

N. Friedman, J. Levin, I. Amit, 2011, Nature Biotechnology.

Systematic Dissection of Roles for Chromatin Regulators in a Yeast Stress Response

N. Friedman, A. Regev, C. Liu, 2012, PLoS biology.

Chromatin conformation governs T-cell receptor Jβ gene segment usage

Wilfred Ndifon, Nir Friedman, Hilah Gal, 2012, Proceedings of the National Academy of Sciences.

Mapping Differentiation under Mixed Culture Conditions Reveals a Tunable Continuum of T Cell Fates

Nir Friedman, Yuval Hart, Avi Mayo, 2013, PLoS biology.

Combined Analysis of Antigen Presentation and T-cell Recognition Reveals Restricted Immune Responses in Melanoma.

Eytan Ruppin, Nir Friedman, Dan Reshef, 2018, Cancer discovery.

The integrated genomic and immune landscapes of lethal metastatic breast cancer (MBC).

Edith M. Ross, N. Friedman, F. Markowetz, 2018 .

Paradoxical Signaling by a Secreted Molecule Leads to Homeostasis of Cell Levels

Nir Friedman, Yuval Hart, Uri Alon, 2014, Cell.

Functional Genomics of Lung Disease

S. Redline, N. Friedman, A. Regev, 2004 .

Quantitative kinetic analysis of the bacteriophage λ genetic network

Nir Friedman, N. Friedman, D. Court, 2005 .

Engineering of synthetic cellular microenvironments: implications for immunity.

N. Friedman, I. Zaretsky, B. Geiger, 2014, Journal of autoimmunity.

Condition-specific genetic interaction maps reveal crosstalk between the cAMP/PKA and the HOG MAPK pathways in the activation of the general stress response

Nir Friedman, Amir Aharoni, Jenia Gutin, 2015, Molecular systems biology.

Endocrine Autoimmune Disease as a Fragility of Immune Surveillance against Hypersecreting Mutants

N. Friedman, U. Alon, A. Mayo, 2019, bioRxiv.

Endocrine Autoimmune Disease as a Fragility of Immune Surveillance against Hypersecreting Mutants

N. Friedman, U. Alon, A. Mayo, 2019, bioRxiv.

The Genomic and Immune Landscapes of Lethal Metastatic Breast Cancer

Edith M. Ross, N. Friedman, F. Markowetz, 2019, Cell reports.

Chromatin Dynamics and the RNA Exosome Function in Concert to Regulate Transcriptional Homeostasis

N. Friedman, L. Steinmetz, Vidya Subramanian, 2015, Cell reports.

Cell Cycle– and Chaperone-Mediated Regulation of H3K56ac Incorporation in Yeast

N. Friedman, C. Liu, M. Grunstein, 2008, PLoS genetics.

Substrate-bound CCL 21 and ICAM 1 combined with soluble IL-6 collectively augment the expansion of antigen-speci fi c murine CD 4 1 T cells

N. Friedman, I. Zaretsky, B. Geiger, 2019 .

Substrate-bound CCL21 and ICAM1 combined with soluble IL-6 collectively augment the expansion of antigen-specific murine CD4+ T cells.

N. Friedman, I. Zaretsky, B. Geiger, 2017, Blood advances.

Cell cycle-and chaperone-mediated regulation of H 3 K 56 ac incorporation in yeast

N. Friedman, C. Liu, M. Grunstein, 2017 .

Aging promotes reorganization of the CD4 T cell landscape toward extreme regulatory and effector phenotypes

N. Friedman, V. Chalifa-Caspi, Esti Yeger-Lotem, 2019, Science Advances.

Statistical methods for analyzing gene expression data for cancer research.

N. Friedman, N. Kaminski, 2002, Ernst Schering Research Foundation workshop.

Stochasticity in Gene Expression as Observed by Single-molecule Experiments in Live Cells

Nir Friedman, Long Cai, N. Friedman, 2010 .

Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting

Nir Friedman, Moisés Goldszmidt, Thomas J. Lee, 1998, ICML.

Dynamic response diversity of NFAT isoforms in individual living cells.

Nir Friedman, Ziv Porat, Eric Shifrut, 2013, Molecular cell.

Subtyping of Small Cell Lung Cancer using plasma cell-free nucleosomes

N. Friedman, A. Zick, R. Sadeh, 2022, bioRxiv.

Precise Temporal Modulation in the Response of the SOS DNA Repair Network in Individual Bacteria

Nir Friedman, Uri Alon, N. Friedman, 2005, PLoS biology.

Collective Decision Making in T Cells

Yaron E. Antebi, N. Friedman, S. Reich-Zeliger, 2022 .

Dominant Sensitive Genes Antibiotics by Phage-Mediated Delivery of Reversing Bacterial Resistance to

N. Friedman, S. Molshanski-Mor, U. Qimron, 2012 .

Reversing Bacterial Resistance to Antibiotics by Phage

N. Friedman, S. Molshanski-Mor, U. Qimron, 2011 .

Modulation of DNA conformations through the formation of alternative high-order HU-DNA complexes.

N. Friedman, A. Oppenheim, C. Vorgias, 2004, Journal of molecular biology.

The DNA Damage Road Map

N. Friedman, M. Schuldiner, 2010, Science.

Avidity optimization of a MAGE‐A1‐specific TCR with somatic hypermutation

N. Friedman, E. Greenstein, E. Tzehoval, 2020, European journal of immunology.

Endocrine Autoimmune Disease as a Fragility of Immune Surveillance against Hypersecreting Mutants

N. Friedman, U. Alon, A. Mayo, 2019, bioRxiv.

A hierarchy of selection pressures determines the organization of the T cell receptor repertoire

N. Friedman, S. Reich-Zeliger, A. Madi, 2022, Frontiers in Immunology.

Modulating the proliferative and cytotoxic properties of human TIL by a synthetic immune niche of immobilized CCL21 and ICAM1

N. Friedman, B. Geiger, Sharon Yunger, 2022, bioRxiv.

Predicting CD4 T-cell epitopes based on antigen cleavage, MHCII presentation, and TCR recognition

N. Friedman, A. Sali, P. Paz, 2018, bioRxiv.

CNS-specific immunity at the choroid plexus shifts toward destructive Th2 inflammation in brain aging

N. Friedman, M. Mattson, M. Schwartz, 2013, Proceedings of the National Academy of Sciences.

Discriminative Learning of Composite Transcriptional Regulatory Modules

N. Friedman, T. Naveh, 2004 .

From pseudotime to true dynamics: reconstructing a real-time axis for T cells differentiation

Yaron E. Antebi, N. Friedman, S. Reich-Zeliger, 2022, bioRxiv.

Criticality in Neuronal Networks

Nir Friedman, Karin A. Dahmen, John M. Beggs, 2012 .

UvA-DARE ( Digital Academic Repository ) Universal Quake Statistics : From Compressed Nanocrystals to Earthquakes

Danijel Schorlemmer, Julia R. Greer, Xin Liu, 2015 .

Induction of CD4 T cell memory by local cellular collectivity

Nir Friedman, Eyal David, Gil Stelzer, 2018, Science.

GLYCO XVIII International Symposium on Glycoconjugates: Abstracts

N. Friedman, S. Mitrani‐Rosenbaum, I. Eisenberg, 2005, Glycoconjugate Journal.

A functional selection model explains evolutionary robustness despite plasticity in regulatory networks

Nir Friedman, Hanah Margalit, Aviv Regev, 2012 .

Subtyping of Small Cell Lung Cancer using plasma cell-free nucleosomes

N. Friedman, A. Zick, R. Sadeh, 2023, bioRxiv.

Proceedings of the 15th annual conference on uncertainty in artificial intelligence (UAI)

Nir Friedman, Abraham J. Wyner, Moises Goldszmidt, 1999 .

Nondeterministic Actions and the Frame Problem

Craig Boutilier, Nir Friedman, Craig Boutilier, 1995 .

A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval

Nir Friedman, Hanah Margalit, Tommy Kaplan, 2008, PLoS Comput. Biol..

CIS: compound importance sampling method for protein-DNA binding site p-value estimation

N. Friedman, G. Elidan, Y Barash, 2005, Bioinform..

A structural EM algorithm for phylogenetic inference

Tal Pupko, Nir Friedman, Itsik Pe'er, 2001, J. Comput. Biol..

De novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity

Michael Ott, Nir Friedman, Aviv Regev, 2013 .

Human and porcine early kidney precursors as a new source for transplantation

N. Friedman, S. Reich-Zeliger, S. Aviel-Ronen, 2003, Nature Medicine.

Dynamic Perturbations of the T-Cell Receptor Repertoire in Chronic HIV Infection and following Antiretroviral Therapy

Nir Friedman, Mahdad Noursadeghi, Niclas Thomas, 2015, Front. Immunol..

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science

Nir Friedman, Jenia Gutin, N. Friedman, 2014 .

Glatiramer acetate reduces Th-17 inflammation and induces regulatory T-cells in the CNS of mice with relapsing–remitting or chronic EAE

N. Friedman, H. Gal, A. Vainshtein, 2010, Journal of Neuroimmunology.

NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport

N. Friedman, N. Rajewsky, Nikos Karaiskos, 2021, Nature Protocols.

Dynamics of Sir3 spreading in budding yeast: secondary recruitment sites and euchromatic localization

N. Friedman, A. Weiner, O. Rando, 2011, The EMBO journal.

Automatic genome-wide reconstruction of phylogenetic gene trees

Avi Pfeffer, Nir Friedman, Aviv Regev, 2007, ISMB/ECCB.

Phylogeny reconstruction: increasing the accuracy of pairwise distance estimation using Bayesian inference of evolutionary rates

Tal Pupko, Nir Friedman, Matan Ninio, 2007, Bioinform..

Gene expression cartography

Nir Friedman, Nikolaus Rajewsky, Mor Nitzan, 2019, Nature.

Inferring quantitative models of regulatory networks from expression data

Nir Friedman, Aviv Regev, Iftach Nachman, 2004, ISMB/ECCB.

Using Bayesian networks to analyze expression data

Michal Linial, Nir Friedman, Iftach Nachman, 2000, RECOMB '00.

Do all creatures possess an acquired immune system of some sort?

N. Friedman, I. Cohen, Jacob Rimer, 2014, BioEssays : news and reviews in molecular, cellular and developmental biology.

Combined presentation and immunogenicity analysis reveals a recurrent RAS.Q61K neoantigen in melanoma.

Ansuman T. Satpathy, N. Friedman, S. Reich-Zeliger, 2021, The Journal of clinical investigation.

Practical approaches to analyzing results of microarray experiments.

Nir Friedman, Naftali Kaminski, N. Friedman, 2002, American journal of respiratory cell and molecular biology.

Learning Module Networks

Nir Friedman, Aviv Regev, Eran Segal, 2002, J. Mach. Learn. Res..

A Gamma mixture model better accounts for among site rate heterogeneity

Itay Mayrose, Tal Pupko, Nir Friedman, 2005, ECCB/JBI.

Strand-specific RNA sequencing reveals extensive regulated long antisense transcripts that are conserved across yeast species

N. Friedman, M. Yassour, J. Levin, 2010, Genome Biology.

Nuclear receptors control pro-viral and antiviral metabolic responses to hepatitis C virus infection.

N. Friedman, Naomi Habib, Y. Nahmias, 2016, Nature chemical biology.

A Systematic Experiment for Studying the Role of Chromatin Remodelers in Transcription Regulation

N. Friedman, 2011 .

From large-scale assays to mechanistic insights: computational analysis of interactions.

Nir Friedman, N. Friedman, A. Jaimovich, 2011, Current opinion in biotechnology.

Data perturbation for escaping local maxima in learning

Dale Schuurmans, Nir Friedman, Gal Elidan, 2002, AAAI/IAAI.

From promoter sequence to expression: a probabilistic framework

Nir Friedman, Daphne Koller, Eran Segal, 2002, RECOMB '02.

Hormone Circuits Lecture notes Uri Alon (Spring 2021) Lecture 10 Autoimmune disease as a fragility of surveillance against hyper-secreting mutants

N. Friedman, U. Alon, A. Mayo, 2019 .

ChIP-seq of plasma cell-free nucleosomes identifies gene expression programs of the cells of origin

N. Friedman, Y. Dor, B. Glaser, 2021, Nature Biotechnology.

ChIP-seq of plasma cell-free nucleosomes identifies gene expression programs of the cells-of-origin

N. Friedman, Y. Dor, B. Glaser, 2021, Nature Biotechnology.

ChIP-seq of plasma cell-free nucleosomes identifies cell-of-origin gene expression programs

N. Friedman, Y. Dor, B. Glaser, 2020 .

Dynamics of Chromatin and Transcription during Transient Depletion of the RSC Chromatin Remodeling Complex

N. Friedman, D. Joseph-Strauss, Alon Appleboim, 2019, Cell reports.

The proteasome regulator PSME4 drives immune evasion and abrogates anti-tumor immunity in NSCLC

Aleksandra A. Kolodziejczyk, A. T. Rêgo, N. Friedman, 2021, bioRxiv.

Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge

Nir Friedman, Hanah Margalit, Tommy Kaplan, 2005, PLoS Comput. Biol..

Predicting Transcription Factor Binding Sites Using Structural Knowledge

Nir Friedman, Hanah Margalit, Tommy Kaplan, 2005, RECOMB.

Early sample tagging and pooling enables simultaneous SARS-CoV-2 detection and variant sequencing

N. Friedman, Naomi Habib, A. Klochendler, 2021, Science Translational Medicine.

Dynamic changes in tRNA modifications and abundance during T cell activation

N. Friedman, Y. Pilpel, S. Reich-Zeliger, 2020, Proceedings of the National Academy of Sciences.

Efficient Exact p-Value Computation for Small Sample, Sparse, and Surprising Categorical Data

Naftali Tishby, Nir Friedman, Gill Bejerano, 2004, J. Comput. Biol..

Physical Module Networks: an integrative approach for reconstructing transcription regulation

Nir Friedman, Aviv Regev, Noa Novershtern, 2011, Bioinform..

High Throughput Determination of TGFβ1/SMAD3 Targets in A549 Lung Epithelial Cells

Oliver Eickelberg, Nir Friedman, Naftali Kaminski, 2011, PloS one.

Quantifying changes in the T cell receptor repertoire during thymic development

N. Friedman, T. Mora, S. Reich-Zeliger, 2023, eLife.

Population based selection shapes the T cell receptor repertoire during thymic development

N. Friedman, T. Mora, S. Reich-Zeliger, 2022, bioRxiv.

Selective flexible packaging pathways of the segmented genome of influenza A virus

N. Friedman, O. Seitz, T. Wolff, 2020, Nature Communications.

Flexibility and constraint in preimplantation gene regulation in mouse

N. Friedman, O. Rando, R. Brewster, 2020, bioRxiv.

A branch-and-bound algorithm for the inference of ancestral amino-acid sequences when the replacement rate varies among sites: Application to the evolution of five gene families

Tal Pupko, Nir Friedman, Dan Graur, 2002, Bioinform..

Exploring transcription regulation through cell-to-cell variability

Nir Friedman, N. Friedman, Ruty Rinott, 2011, Proceedings of the National Academy of Sciences.

Genome-wide histone modification patterns in Kluyveromyces Lactis reveal evolutionary adaptation of a heterochromatin-associated mark

N. Friedman, A. Weiner, O. Rando, 2016, bioRxiv.

A branch-and-bound algorithm for the inference of ancestral amino-acid sequences when the replacement rate varies among sites

N. Friedman, I. Pe’er, T. Pupko, 2002 .

Learning Hidden Variable Networks: The Information Bottleneck Approach

Nir Friedman, Gal Elidan, N. Friedman, 2005, J. Mach. Learn. Res..

Fine-Resolution Mapping of TF Binding and Chromatin Interactions

N. Friedman, Oren Ram, R. Sadeh, 2018, Cell reports.

RNA polymerase mapping during stress responses reveals widespread nonproductive transcription in yeast

N. Friedman, M. Yassour, C. Liu, 2010, Genome Biology.

Myocardial infarction triggers cardioprotective antigen-specific T helper cell responses.

N. Friedman, B. Ludewig, J. Piek, 2019, The Journal of clinical investigation.

Class discovery in gene expression data

Nir Friedman, Zohar Yakhini, Amir Ben-Dor, 2001, RECOMB.

Tissue classification with gene expression profiles.

Nir Friedman, Zohar Yakhini, Iftach Nachman, 2000 .

Modularity and directionality in genetic interaction maps

Nir Friedman, Hanah Margalit, Ruty Rinott, 2010, Bioinform..

Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm

Nir Friedman, Iftach Nachman, Dana Pe'er, 1999, UAI.

Data Analysis with Bayesian Networks: A Bootstrap Approach

Nir Friedman, Moisés Goldszmidt, Abraham J. Wyner, 1999, UAI.

Transcription feedback dynamics in the wake of cytoplasmic mRNA degradation shutdown

N. Friedman, D. Joseph-Strauss, A. Chappleboim, 2021, bioRxiv.

Diverse continuum of CD4+ T-cell states is determined by hierarchical additive integration of cytokine signals

N. Friedman, S. Reich-Zeliger, I. Zaretsky, 2017, Proceedings of the National Academy of Sciences.

T-Cell Vaccination: An Insight Into T-Cell Regulation

N. Friedman, I. Cohen, F. Quintana, 2016 .

A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites

Nir Friedman, Gill Bejerano, Yoseph Barash, 2001, WABI.

Tracking global changes induced in the CD4 T-cell receptor repertoire by immunization with a complex antigen using short stretches of CDR3 protein sequence

John Shawe-Taylor, Nir Friedman, Niclas Thomas, 2014, bioRxiv.

McPAS‐TCR: a manually curated catalogue of pathology‐associated T cell receptor sequences

Jaime Prilusky, Nir Friedman, Nili Tickotsky, 2017, Bioinform..

Comprehensive mapping of DNA damage: from static genetic maps to condition-specific maps.

N. Friedman, 2013, Molecular cell.

Quantitative kinetic analysis of the bacteriophage lambda genetic network.

Nir Friedman, N. Friedman, D. Court, 2005, Proceedings of the National Academy of Sciences of the United States of America.

Studying gene expression at the level of the single cell.

N. Friedman, 2013, Briefings in functional genomics.

Reversing Bacterial Resistance to Antibiotics by Phage-Mediated Delivery of Dominant Sensitive Genes

N. Friedman, S. Molshanski-Mor, U. Qimron, 2011, Applied and Environmental Microbiology.

Perforin-Positive Dendritic Cells Exhibit an Immuno-regulatory Role in Metabolic Syndrome and Autoimmunity.

N. Friedman, S. Reich-Zeliger, Steffen Jung, 2015, Immunity.

Mitochondrial processes are impaired in hereditary inclusion body myopathy.

N. Friedman, Zohar Itzhaki, Noa Novershtern, 2008, Human molecular genetics.

Blood transcriptional signatures of multiple sclerosis: Unique gene expression of disease activity

Nir Friedman, Naftali Kaminski, Anat Achiron, 2004, Annals of neurology.

Transcriptional profiling of non-small cell lung cancer using oligonucleotide microarrays.

N. Friedman, Z. Yakhini, A. Ben-Dor, 2002, Chest.

UAI '02, Proceedings of the 18th Conference in Uncertainty in Artificial Intelligence, University of Alberta, Edmonton, Alberta, Canada, August 1-4, 2002

Nir Friedman, Adnan Darwiche, Adnan Darwiche, 2002, Conference on Uncertainty in Artificial Intelligence.

Tissue classification with gene expression profiles

Nir Friedman, Zohar Yakhini, Amir Ben-Dor, 2000, RECOMB '00.

CIS: compound importance sampling method for protein-DNA binding site p-value estimation

Nir Friedman, Gal Elidan, Yoseph Barash, 2005, Bioinform..

Efficient learning using constrained sufficient statistics

Lise Getoor, Nir Friedman, N. Friedman, 1999, AISTATS.

Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays

Nir Friedman, Naftali Kaminski, Yoseph Barash, 2004, Bioinform..

Balancing speed and accuracy of polyclonal T cell activation: a role for extracellular feedback

Nir Friedman, Tsvi Tlusty, Yonatan Savir, 2012, BMC Systems Biology.

From pseudo to real-time dynamics of T cell thymic differentiation

Yaron E. Antebi, N. Friedman, S. Reich-Zeliger, 2022, iScience.

Gene expression cartography

N. Friedman, N. Rajewsky, Nikos Karaiskos, 2019, Nature.

Charting a tissue from single-cell transcriptomes

Nir Friedman, Nikolaus Rajewsky, Mor Nitzan, 2018 .

Modulating the proliferative and cytotoxic properties of patient-derived TIL by a synthetic immune niche of immobilized CCL21 and ICAM1

N. Friedman, B. Geiger, Sharon Yunger, 2023, Frontiers in Oncology.

Expansion and Antitumor Cytotoxicity of T-Cells Are Augmented by Substrate-Bound CCL21 and Intercellular Adhesion Molecule 1

N. Friedman, B. Geiger, Shimrit Adutler-Lieber, 2018, Front. Immunol..

Substantial Histone Reduction Modulates Genomewide Nucleosomal Occupancy and Global Transcriptional Output

N. Friedman, J. Ragoussis, A. Weiner, 2011, PLoS biology.

Context-specific Bayesian clustering for gene expression data

Nir Friedman, Yoseph Barash, N. Friedman, 2001, J. Comput. Biol..

Coupled IL-2–Dependent Extracellular Feedbacks Govern Two Distinct Consecutive Phases of CD4 T Cell Activation

Nir Friedman, Benjamin M. Chain, Nir Waysbort, 2013, The Journal of Immunology.

Robust temporal and spectral modeling for query By melody

Shlomo Dubnov, Yoram Singer, Shai Shalev-Shwartz, 2002, SIGIR '02.

Clonal expansion under the microscope: studying lymphocyte activation and differentiation using live‐cell imaging

Nir Friedman, N. Friedman, M. Polonsky, 2016, Immunology and cell biology.

Chromatin immunoprecipitation and sequencing of histone modifications on blood-circulating

N. Friedman, Y. Dor, B. Glaser, 2022 .

Dynamic single-cell measurements of gene expression in primary lymphocytes: challenges, tools and prospects.

N. Friedman, I. Zaretsky, M. Polonsky, 2013, Briefings in functional genomics.

Belief Revision with Unreliable Observations

Craig Boutilier, Joseph Y. Halpern, Nir Friedman, 1998, AAAI/IAAI.

Autonomous TNF is critical for in vivo monocyte survival in steady state and inflammation

N. Friedman, I. Amit, Eyal David, 2017, The Journal of experimental medicine.

Mapping the Landscape of a Eukaryotic Degronome.

N. Friedman, R. Sadeh, Y. Geffen, 2016, Molecular cell.

Monitoring the dynamics of primary T cell activation and differentiation using long term live cell imaging in microwell arrays.

Nir Friedman, Guy Aidelberg, Eric Shifrut, 2012, Lab on a chip.

Replication and Active Demethylation Represent Partially Overlapping Mechanisms for Erasure of H3K4me3 in Budding Yeast

N. Friedman, S. Schreiber, C. Liu, 2010, PLoS genetics.

Plausibility measures and default reasoning

Joseph Y. Halpern, Nir Friedman, N. Friedman, 1996, JACM.

cross-tolerance in the heart memory : a lesson from thermal responses and ischemic Physiological and molecular evidence of heat acclimation

N. Friedman, I. Wapinski, M. Stern, 2008 .

Clk post-transcriptional control denoises circadian transcription both temporally and spatially

N. Friedman, S. Kadener, M. Rosbash, 2015, Nature Communications.

A synthetic biology approach to probing nucleosome symmetry

N. Friedman, N. Kelleher, D. Bolon, 2017, bioRxiv.

First-order conditional logic for default reasoning revisited

Joseph Y. Halpern, Nir Friedman, Daphne Koller, 2000, TOCL.

Plausibility measures and default reasoning: an overview

Joseph Y. Halpern, Nir Friedman, N. Friedman, 1999, Proceedings. 14th Symposium on Logic in Computer Science (Cat. No. PR00158).

Modeling Belief in Dynamic Systems, Part II: Revisions and Update

Joseph Y. Halpern, Nir Friedman, N. Friedman, 1999, J. Artif. Intell. Res..

A Qualitative Markov Assumption and Its Implications for Belief Change pdf

Joseph Y. Halpern, Nir Friedman, N. Friedman, 1996, UAI.

Elucidating Combinatorial Chromatin States by Co-ImmunoPrecipitation (comb-ChIP)

N. Friedman, A. Rahat, Ronen Sadeh, 2016, bioRxiv.

Molecular constraints on CDR3 for thymic selection of MHC-restricted TCRs from a random pre-selection repertoire

Nir Friedman, Eric Shifrut, Peter D Sun, 2019, Nature Communications.

Physiological and molecular evidence of heat acclimation memory: a lesson from thermal responses and ischemic cross-tolerance in the heart.

N. Friedman, I. Wapinski, M. Stern, 2008, Physiological genomics.

Statistics of dislocation slip avalanches in nanosized single crystals show tuned critical behavior predicted by a simple mean field model.

N. Friedman, K. Dahmen, G. Tsekenis, 2012, Physical review letters.

Learning Probabilistic Models of Link Structure

Ben Taskar, Lise Getoor, Nir Friedman, 2003, J. Mach. Learn. Res..

CD4+ T Cell-Receptor Repertoire Diversity is Compromised in the Spleen but Not in the Bone Marrow of Aged Mice Due to Private and Sporadic Clonal Expansions

Wilfred Ndifon, Nir Friedman, Hilah Gal, 2013, Front. Immunol..

On Decision-Theoretic Foundations for Defaults

Ronen I. Brafman, Nir Friedman, N. Friedman, 1995, IJCAI.

Correction of T-Cell Repertoire and Autoimmune Diabetes in NOD Mice by Non-myeloablative T-Cell Depleted Allogeneic HSCT

N. Friedman, S. Reich-Zeliger, Y. Reisner, 2023, Stem cells translational medicine.

Structured Representation of Complex Stochastic Systems

Avi Pfeffer, Nir Friedman, Daphne Koller, 1998, AAAI/IAAI.

Belief Revision: A Critique

Joseph Y. Halpern, Nir Friedman, N. Friedman, 1996, J. Log. Lang. Inf..

A Knowledge-Based Framework for Belief Change, Part II: Revision and Update

Joseph Y. Halpern, Nir Friedman, N. Friedman, 1994, KR.

Towards an Integrated Protein-Protein Interaction Network: A Relational Markov Network Approach

Nir Friedman, Hanah Margalit, Gal Elidan, 2006, J. Comput. Biol..

The proteasome regulator PSME4 modulates proteasome activity and antigen diversity to abrogate antitumor immunity in NSCLC

Aleksandra A. Kolodziejczyk, A. T. Rêgo, N. Friedman, 2023, Nature Cancer.

N. Friedman, I. Wapinski, E. Kodesh, 2004, Journal of applied physiology.

"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks

Nir Friedman, Gal Elidan, Iftach Nachman, 2007, J. Mach. Learn. Res..

Data mining tasks and methods: Classification: Bayesian classification

Ron Kohavi, Nir Friedman, N. Friedman, 2002 .

Antigen experience relaxes the organisational structure of the T cell receptor repertoire

N. Friedman, S. Reich-Zeliger, A. Madi, 2021, bioRxiv.

Temporal profiling of redox-dependent heterogeneity in single cells

N. Friedman, M. Schuldiner, D. Reichmann, 2017, bioRxiv.

Conditional Logics of Belief Change

Joseph Y. Halpern, Nir Friedman, N. Friedman, 1994, AAAI.

Conserved degronome features governing quality control associated proteolysis

K. E. Johansson, N. Friedman, K. Lindorff-Larsen, 2022, bioRxiv.

Viral infection reveals hidden sharing of TCR CDR3 sequences between individuals

N. Friedman, S. Reich-Zeliger, A. Madi, 2023, Frontiers in Immunology.

Molecular constraints on CDR3 for thymic selection of MHC-restricted TCRs from a random pre-selection repertoire

N. Friedman, Marco Craveiro, Eric Shifrut, 2019, Nature Communications.

Universal Quake Statistics: From Compressed Nanocrystals to

Danijel Schorlemmer, Julia R. Greer, Nir Friedman, 2015 .

GeneXPress : A Visualization and Statistical Analysis Tool for Gene Expression and Sequence Data

D. Koller, N. Friedman, A. Regev, 2022 .

Agglomerative Multivariate Information Bottleneck

Naftali Tishby, Nir Friedman, Noam Slonim, 2001, NIPS.

Building Classifiers Using Bayesian Networks

Nir Friedman, Moisés Goldszmidt, M. Goldszmidt, 1996, AAAI/IAAI, Vol. 2.

On the application of the bootstrap for computing confidence measures on features of induced Bayesian networks

Nir Friedman, Moisés Goldszmidt, Abraham J. Wyner, 1999, AISTATS.

Dynamics of Replication-Independent Histone Turnover in Budding Yeast

N. Friedman, Michael F. Dion, O. Rando, 2007, Science.

UvA-DARE ( Digital Academic Repository ) Chromatin dynamics in yeast : The RITE assay for histone turnover and inheritance

N. Friedman, O. Rando, K. Verzijlbergen, 2011 .

Polyclonal lymphoid expansion drives paraneoplastic autoimmunity in neuroblastoma.

David L. Gibbs, N. Friedman, A. Naranjo, 2023, Cell reports.

Blood mononuclear cell gene expression signature of postpartum depression

N. Friedman, L. Canetti, T. Goltser-Dubner, 2010, Molecular Psychiatry.

Extrachromosomal DNA Amplification Contributes to Small Cell Lung Cancer Heterogeneity and is Associated with Worse Outcomes.

N. Friedman, Y. Pommier, M. Aladjem, 2023, Cancer discovery.

An integrative clustering and modeling algorithm for dynamical gene expression data

Nir Friedman, Julia Sivriver, Naomi Habib, 2011, Bioinform..

Likelihood Computations Using Value Abstraction

Nir Friedman, Dan Geiger, Noam Lotner, 2000, UAI.

Probabilistic models for identifying regulation networks

Nir Friedman, N. Friedman, 2003, ECCB.

Tracking global changes induced in the CD4 T cell receptor repertoire by immunisation with a complex antigen using local sequence features of CDR3 protein sequence

John Shawe-Taylor, Nir Friedman, Niclas Thomas, 2014 .

De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

Colin N. Dewey, Philip D. Blood, Richard D. LeDuc, 2013, Nature Protocols.

Physical Module Networks: an integrative approach for reconstructing transcription regulation

N. Friedman, A. Regev, Noa Novershtern, 2011, Bioinform..

Phylogeny reconstruction: increasing the accuracy of pairwise distance estimation using Bayesian inference of evolutionary rates

N. Friedman, T. Pupko, M. Ninio, 2007, Bioinform..

From signatures to models: understanding cancer using microarrays

D. Koller, N. Friedman, A. Regev, 2005, Nature Genetics.

Mean Field Variational Approximation for Continuous-Time Bayesian Networks pdf

Nir Friedman, Raz Kupferman, Tal El-Hay, 2009, J. Mach. Learn. Res..

Gaussian Process Networks

Nir Friedman, Iftach Nachman, N. Friedman, 2000, UAI.

Discovering the Hidden Structure of Complex Dynamic Systems

Xavier Boyen, Nir Friedman, Daphne Koller, 1999, UAI.

Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm

D. Pe’er, N. Friedman, I. Nachman, 1999, UAI.

Learning the Structure of Dynamic Probabilistic Networks

Stuart J. Russell, N. Friedman, Kevin P. Murphy, 1998, UAI.

Discovering Hidden Variables: A Structure-Based Approach

Nir Friedman, Daphne Koller, Gal Elidan, 2000, NIPS.

Glatiramer acetate reduces Th-17 inflammation and induces regulatory T-cells in the CNS of mice with relapsing–remitting or chronic EAE

N. Friedman, H. Gal, A. Vainshtein, 2010, Journal of Neuroimmunology.

Scoring Genes for Relevance

Nir Friedman, Zohar Yakhini, Amir Ben-Dor, 2000 .

Elucidating Combinatorial Chromatin States at Single-Nucleosome Resolution

N. Friedman, R. Sadeh, A. Rahat, 2016, Molecular cell.

Probabilistic Graphical Models - Principles and Techniques

D. Koller, N. Friedman, 2009 .

Learning Probabilistic Relational Models with Structural Uncertainty

Lise Getoor, Nir Friedman, Daphne Koller, 2000 .

Universal Quake Statistics: From Compressed Nanocrystals to Earthquakes

Braden A. W. Brinkman, N. Friedman, K. Dahmen, 2015, Scientific Reports.

Predicting CD4 T-cell epitopes based on antigen cleavage, MHCII presentation, and TCR recognition

N. Friedman, A. Sali, P. Paz, 2018, bioRxiv.

De novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity

Colin N. Dewey, Philip D. Blood, Richard D. LeDuc, 2013 .

The DNA Damage Road Map

N. Friedman, M. Schuldiner, 2010, Science.

Learning the Dimensionality of Hidden Variables

Nir Friedman, Gal Elidan, N. Friedman, 2001, UAI.

ChIP-seq of plasma cell-free nucleosomes identifies cell-of-origin gene expression programs

N. Friedman, Y. Dor, B. Glaser, 2019, bioRxiv.

Polyclonal lymphoid expansion drives paraneoplastic autoimmunity in neuroblastoma

David L. Gibbs, N. Friedman, A. Naranjo, 2021, bioRxiv.