Interplay between parallel and diagonal electronic nematic phases in interacting systems (2006)
Doh, Hyeonjin, Friedman, Nir, Kee, Hae-Young
An electronic nematic phase can be classified by a spontaneously broken discrete rotational symmetry of a host lattice. In a square lattice, there are two distinct nematic phases. The parallel...
Single-Nucleosome Mapping of Histone Modifications in S. cerevisiae (2005)
Chih Long Liu, Tommy Kaplan, Minkyu Kim, Stephen Buratowski, Stuart L. Schreiber, Nir Friedman, ...
High-resolution microarrays were used to investigate 12 histone modifications across thousands of yeast nucelosomes in vivo. Two main groups co-occurred, consistent with the redundant histone code...
Single-Nucleosome Mapping of Histone Modifications in S. cerevisiae (2005)
Chih Long Liu, Tommy Kaplan, Minkyu Kim, Stephen Buratowski, Stuart L. Schreiber, Nir Friedman, ...
Covalent modification of histone proteins plays a role in virtually every process on eukaryotic DNA, from transcription to DNA repair. Many different residues can be covalently modified, and it has...
Nir Friedman, Shuki Vardi, Michal Ronen, Uri Alon, Joel Stavans
Oscillations in transcriptional activity in the network responsible for controlling DNA damage are monitored with GFP- promoter fusions in individual E. coli cells.
Nir Friedman, Shuki Vardi, Michal Ronen, Uri Alon, Joel Stavans
The SOS genetic network is responsible for the repair/bypass of DNA damage in bacterial cells. While the initial stages of the response have been well characterized, less is known about the dynamics...
Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge (2005)
Tommy Kaplan, Nir Friedman, Hanah Margalit
Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach applicable...
Atom-Optics Billiards: Non-linear dynamics with cold atoms in optical traps (2004)
Kaplan, Ariel, Andersen, Mikkel, Friedman, Nir, Davidson, Nir
We present a new experimental system (the ``atom-optics billiard'') and demonstrate chaotic and regular dynamics of cold, optically trapped atoms. We show that the softness of the walls and...
Learning Probabilistic Models of Relational Structure (2003)
Lise Getoor, Nir Friedman, Benjamin Taskar
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with "flat" data representations, forcing us to convert our data into a form that loses much of...
The Information Bottleneck EM Algorithm (2003)
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation...
PCluster: Probabilistic Agglomerative Clustering of Gene (2003)
A central problem in analysis of gene expression data is clustering of genes with similar expression profiles. In this paper, I describe an hierarchical clustering procedure that is based on simple...
Modeling Belief in Dynamic Systems, Part I: Foundations (2003)
Friedman, Nir, Halpern, Joseph Y.
Belief change is a fundamental problem in AI: Agents constantly have to update their beliefs to accommodate new observations. In recent years, there has been much work on axiomatic characterizations...
Modeling Belief in Dynamic Systems, Part II: Revisions and Update (2003)
Friedman, Nir, Halpern, Joseph Y.
The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a...
Data Perturbation for Escaping Local Maxima in Learning (2003)
Gal Elidan, Matan Ninio, Nir Friedman, Dale Schuurmans
Almost all machine learning algorithms---be they for regression, classification or density estimation---seek hypotheses that optimize a score on training data. In most interesting cases, however,...
The Information Bottleneck EM Algorithm (2003)
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation...
Modeling Dependencies in Protein-DNA Binding Sites (2003)
Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan
The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and...
Learning Module Networks (2003)
Eran Segal, Bauer Ctr, Daphne Koller, Nir Friedman
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and...
The Information Bottleneck EM Algorithm (2003)
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation...
Learning Module Networks (2003)
Eran Segal, Bauer Ctr, Daphne Koller, Nir Friedman
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and...
Module Networks: Discovering Regulatory (2003)
Eran Segal, David Botstein, Daphne Koller, Nir Friedman
Introduction The complex functions of a living cell are carried out through the concerted activity of many genes and gene products. This activity is often coordinated by the organization of Computer...
Updated August 29, 1999 (2003)
Bayesian classification addresses the classification problem by learning the distribution of instances given different class values. We review the basic notion of Bayesian classification, describe in...
Modeling Dependencies in Protein-DNA Binding Sites (2003)
Yoseph Barash, Gal Elidan, Nir Friedman, Tommy Kaplan
The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and...
Overabundance Analysis and Class Discovery in Gene (2002)
Amir Ben-dor, Nir Friedman, Zohar Yakhini
Recent studies (Alizadeh et al. 2000, Bittner et al. 2000, Golub et al. 1999) demonstrate the discovery of disease subtypes from gene expression data. In this paper, we propose a principled and...
Overabundance Analysis and Class Discovery in Gene (2002)
Amir Ben-dor, Nir Friedman, Zohar Yakhini
Recent studies (Alizadeh et al. 2000, Bittner et al. 2000, Golub et al. 1999) demonstrate the discovery of disease subtypes from gene expression data. In this paper, we propose a principled and...
Practical Approaches to Analyzing Results of Microarray Experiments (2002)
Naftali Kaminski, Nir Friedman
this article we provide a practically oriented review focusing on methods for analysis of large-scale gene expression data in the research laboratory. We describe the various common clustering...
Stable regions and singular trajectories in chaotic soft wall billiards (2002)
Kaplan, Ariel, Friedman, Nir, Andersen, Mikkel, Davidson, Nir
We present numerical and experimental results for the development of islands of stability in atom-optics billiards with soft walls. As the walls are soften, stable regions appear near singular...
Unsupervised Document Classification Using Sequential Information Maximization (2002)
Noam Slonim, Nir Friedman, Naftali Tishby
We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential (sIB) approach is...
Tal Pupko, Dan Graur, Nir Friedman
Motivation: We developed an algorithm to reconstruct ancestral sequences, taking into account the rate variation among sites of the protein sequences. Our algorithm maximizes the joint probability of...
Likelihood Computations Using Value Abstraction (2002)
Nir Friedman, Dan Geiger, Noam Lotner
In this paper, we use evidence-specific value abstraction for speeding Bayesian networks inference.
Robust Temporal and Spectral Modeling for (2002)
Shai Shalev-shwartz, Shlomo Dubnov, Nir Friedman, Yoram Singer
Query by melody is the problem of retrieving musical performances from melodies. Retrieval of real performances is complicated due to the large number of variations in performing a melody and the...
A Structural EM Algorithm for Phylogenetic Inference (2002)
Nir Friedman, Matan Ninio, Tal Pupko
A central task in the study of evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. A well supported approach to tree reconstruction is by maximum likelihood...
Branch-and-Bound Reconstrucion 1 Branch-and-Bound Reconstruction of Ancestral Sequences (2002)
Introduction The problem of ancestral sequence reconstruction is the statistical inference of sequences that correspond to internal nodes in a phylgenetic tree [1]. Joint reconstruction is the task...
On Decision-Theoretic Foundations for Defaults (2002)
Ronen I. Brafman, Nir Friedman
In recent years, considerable effort has gone into understanding default reasoning. Most of this effort concentrated on the question of entailment, i.e., what conclusions are warranted by a...
Unsupervised Document Classification Using Sequential Information Maximization (2002)
Noam Slonim, Nir Friedman, Naftali Tishby
We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the Agglomerative IB algorithm, the new sequential (sIB) approach is...
From Promoter Sequence to Expression: A Probabilistic Framework (2002)
Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Keller
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key...
First-Order Conditional Logic Revisited* (2002)
Nir Friedman, Joseph Y. Halpern, Daphne Koller
Conditional logics play an important role in recent attempts to formulate theories of default reasoning. This paper investigates first-order conditional logic. We show that, as for first-order...
Data Perturbation for Escaping Local Maxima in Learning (2002)
Gal Elidan, Matan Ninio, Nir Friedman, Dale Schuurmans
Almost all machine learning algorithms -- be they for regression, classification or density estimation -- seek hypotheses that optimize a score on training data. In most interesting cases, however,...
A Structural EM Algorithm for Phylogenetic Inference (2002)
Nir Friedman, Matan Ninio, Tal Pupko
A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. The most established approach to tree reconstruction is maximum...
Context-Specific Bayesian Clustering for Gene Expression Data (2002)
The recent growth in genomic data and measurements of genome-wide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors.
Tal Pupko, Masami Hasegawa, Dan Graur, Nir Friedman
Motivation: We developed an algorithm to reconstruct ancestral sequences, taking into account the rate variation among sites of the protein sequences. Our algorithm maximizes the joint probability of...
Using Bayesian Networks to Analyze Expression Data (2002)
Nir Friedman, Michal Linial, Iftach Nachman
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the cell. A major challenge in...
A Structural EM Algorithm for Phylogenetic Inference (2002)
Nir Friedman, Matan Ninio, Tal Pupko
A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. The most established approach to tree reconstruction is maximum...
Learning Probabilistic Models of Relational Structure (2002)
Lise Getoor, Nir Friedman, Benjamin Taskar
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with "flat" data representations, forcing us to convert our data into a form that loses much of...
From Promoter Sequence to Expression: A Probabilistic Framework (2002)
Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Koller
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key...
Context-Specific Independence in Bayesian Networks (2002)
Craig Boutilier, Nir Friedman, Moises Goldszmidt, Daphne Koller
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution,...
From Promoter Sequence to Expression: (2002)
Eran Segal, Yoseph Barash, Itamar Simon, Nir Friedman, Daphne Koller
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key...
Agglomerative Multivariate Information Bottleneck (2002)
Noam Slonim, Nir Friedman, Naftali Tishby
The information bottleneck method is an unsupervised model independent data organization technique. Given a joint distribution P (A, B), this method constructs a new variable T that extracts...
A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites (2002)
Yoseph Barash, Gill Bejerano, Nir Friedman
A central issue in molecular biology is understanding the regulatory mechanisms that control gene expression. The recent ood of genomic and post-genomic data opens the way for computational methods...
Context-Specific Bayesian Clustering for Gene Expression Data (2002)
The recent growth in genomic data and measurements of genome-wide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors.
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm (2002)
Nir Friedman, Iftach Nachman, Dana Peer
Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing...
Using Bayesian Networks to Analyze Expression Data (2002)
Nir Friedman, Michal Linial, Iftach Nachman
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of the cell's transcriptions. A major challenge in computational...
Multivariate Information Bottleneck (2001)
Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby
The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distribution P (A; B), this method constructs a new variable T that extracts partitions,...
Discovering Hidden Variables: A Structure-Based Approach (2001)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Learning the Dimensionality of Hidden Variables (2001)
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Detecting hidden...
Agglomerative Multivariate Information Bottleneck (2001)
Noam Slonim, Nir Friedman, Naftali Tishby
The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distribution P (A; B), this method constructs a new variable T that extracts partitions,...
Multivariate Information Bottleneck (2001)
Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby
The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distribution P (A; B), this method constructs a new variable T that extracts partitions,...
Multivariate Information Bottleneck (2001)
Nir Friedman, Ori Mosenzon, Noam Slonim, Naftali Tishby
The Information bottleneck method is an unsupervised non-parametric data organization technique. Given a joint distribution P(A,B), this method constructs a new variable T that extracts partitions,...
Learning the Dimensionality of Hidden Variables (2001)
A serious problem in learning probabilistic models
Global variational approximation methods in graphical
. In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g., whether two variables are in direct interaction. We can represent...
Rich Probabilistic Models for Gene Expression (2001)
Eran Segal, Ben Taskar, Audrey Gasch, Nir Friedman, Daphne Koller
Clustering is commonly used for analyzing gene expression data. Despite their successes, clustering methods suffer from a number of limitations. First, these methods reveal similarities that exist...
Belief Revision: A Critique (2001)
Friedman, Nir, Halpern, Joseph Y.
We examine carefully the rationale underlying the approaches to belief change taken in the literature, and highlight what we view as methodological problems. We argue that to study belief change...
Using Bayesian Networks to Analyze Expression Data (2001)
Nir Friedman, Michal Linial, Iftach Nachman
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of the cell's transcriptions. A major challenge in computational...
Using Bayesian Networks to Analyze Expression Data (2001)
Nir Friedman, Michal Linial, Iftach Nachman
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the cell. A major challenge in...
Tissue Classification with Gene Expression Profiles (2001)
Amir Ben-dor, Laurakay Bruhn, Nir Friedman, Iftach Nachman, Mich El Schummer, Zohar Yakhini
Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer related cellular processes. Gene expression data is also A preliminary...
Discovering Hidden Variables: A Structure-Based Approach (2001)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Discovering Hidden Variables: (2001)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
Class Discovery in Gene Expression Data (2001)
Recent studies (Alizadeh et al, [1]; Bittner et al,[5]; Golub et al, [11]) demonstrate the discovery of putative disease subtypes from gene expression data. The underlying computational problem is to...
A Structural EM Algorithm for Phylogenetic Inference (2001)
Nir Friedman, Matan Ninio, Tal Pupko
A central task in the study of evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. A well supported approach to tree reconstruction performs maximum likelihood...
Context-Specific Bayesian Clustering for Gene Expression Data (2001)
The recent growth in genomic data and measurement of genomewide expression patterns allows to examine gene regulation by transcription factors using computational tools. In this work, we present a...
A Structural EM Algorithm for Phylogenetic Inference (2000)
Nir Friedman, Matan Ninio, Tal Pupko
A central task in the study of evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. A well supported approach to tree reconstruction is by maximum likelihood...
Learning Probabilistic Relational Models with Structural Uncertainty (2000)
Lise Getoor, Daphne Koller, Benjamin Taskar, Nir Friedman
Most real-world data is stored in relational form. In contrast, most statistical learning methods, e.g., Bayesian network learning, work only with "flat" data representations, forcing us to convert...
Modeling Belief in Dynamic Systems. Part I: Foundations (2000)
Nir Friedman, Joseph Y. Halpern
Belief change is a fundamental problem in AI: Agents constantly have to update their beliefs to accommodate new observations. In recent years, there has been much work on axiomatic characterizations...
Modeling Belief in Dynamic Systems. Part II: Revision and Update (2000)
Nir Friedman, Joseph Y. Halpern
The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a...
Being Bayesian About Network Structure (2000)
In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., whether one variable is a direct parent of the other. Bayesian model selection attempts to find the...
Learning Probabilistic Relational Models (2000)
Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer
A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat" data representations. Thus, to apply these...
Being Bayesian about Network Structure (2000)
In many domains, we are interested in analyzing the structure of the underlying distribution, e.g., whether one variable is a direct parent of the other. Bayesian model-selection attempts to find the...
Discovering Hidden Variables: A Structure-Based Approach (2000)
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce...
From Instances to Classes in Probabilistic Relational Models (2000)
Lise Getoor, Daphne Koller, Nir Friedman
Probabilistic graphical models, in particular Bayesian networks, are useful models for representing statistical patterns in propositional domains. Recent work develops effective techniques for...
Gaussian Process Networks (2000)
In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different candidate structures....
Belief Revision: A Critique (2000)
Nir Friedman, Joseph Y. Halpern
We examine carefully the rationale underlying the approaches to belief change taken in the literature, and highlight what we view as methodological problems. We argue that to study belief change...