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Nir Friedman

Publications and source records attributed to Nir Friedman.

7 recordsLinked to original sources
PubMed2003

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

Much of a cell's activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.

Algorithms↗
PubMed2002

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

Kidney transplantation has been one of the major medical advances of the past 30 years. However, tissue availability remains a major obstacle. This can potentially be overcome by the use of undifferentiated or partially developed kidney precursor cells derived from early embryos and fetal tissue. Here, transplantation in mice reveals the earliest gestational time point at which kidney precursor cells, of both human and pig origin, differentiate into functional nephrons and not into other, non-renal professional cell types. Moreover, successful organogenesis is achieved when using the early kidney precursors, but not later-gestation kidneys. The formed, miniature kidneys are functional as evidenced by the dilute urine they produce. In addition, decreased immunogenicity of the transplants of early human and pig kidney precursors compared with adult kidney transplants is demonstrated in vivo. Our data pinpoint a window of human and pig kidney organogenesis that may be optimal for transplantation in humans.

Adult↗
PubMed2002

Context-specific Bayesian clustering for gene expression data.

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. In this work, we present a class of mathematical models that help in understanding the connections between transcription factors and functional classes of genes based on genetic and genomic data. Such a model represents the joint distribution of transcription factor binding sites and of expression levels of a gene in a unified probabilistic model. Learning a combined probability model of binding sites and expression patterns enables us to improve the clustering of the genes based on the discovery of putative binding sites and to detect which binding sites and experiments best characterize a cluster. To learn such models from data, we introduce a new search method that rapidly learns a model according to a Bayesian score. We evaluate our method on synthetic data as well as on real life data and analyze the biological insights it provides. Finally, we demonstrate the applicability of the method to other data analysis problems in gene expression data.

Bayes Theorem↗
PubMed2002

A structural EM algorithm for phylogenetic inference.

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 likelihood (ML) analysis. Unfortunately, searching for the maximum likelihood phylogenetic tree is computationally prohibitive for large data sets. In this paper, we describe a new algorithm that uses Structural Expectation Maximization (EM) for learning maximum likelihood phylogenetic trees. This algorithm is similar to the standard EM method for edge-length estimation, except that during iterations of the Structural EM algorithm the topology is improved as well as the edge length. Our algorithm performs iterations of two steps. In the E-step, we use the current tree topology and edge lengths to compute expected sufficient statistics, which summarize the data. In the M-Step, we search for a topology that maximizes the likelihood with respect to these expected sufficient statistics. We show that searching for better topologies inside the M-step can be done efficiently, as opposed to standard methods for topology search. We prove that each iteration of this procedure increases the likelihood of the topology, and thus the procedure must converge. This convergence point, however, can be a suboptimal one. To escape from such "local optima," we further enhance our basic EM procedure by incorporating moves in the flavor of simulated annealing. We evaluate these new algorithms on both synthetic and real sequence data and show that for protein sequences even our basic algorithm finds more plausible trees than existing methods for searching maximum likelihood phylogenies. Furthermore, our algorithms are dramatically faster than such methods, enabling, for the first time, phylogenetic analysis of large protein data sets in the maximum likelihood framework.

Algorithms↗
PubMed2002

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.

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 the ancestral sequences, assuming that the rate is gamma distributed among sites. Our algorithm probably finds the global maximum. The use of 'joint' reconstruction is motivated by studies that use the sequences at all the internal nodes in a phylogenetic tree, such as, for instance, the inference of patterns of amino-acid replacement, or tracing the biochemical changes that occurred during the evolution of a given protein family. RESULTS: We give an algorithm that guarantees finding the global maximum. The efficient search method makes our method applicable to datasets with large number sequences. We analyze ancestral sequences of five gene families, exploring the effect of the amount of among-site-rate-variation, and the degree of sequence divergence on the resulting ancestral states. AVAILABILITY AND SUPPLEMENTARY INFORMATION: http://evolu3.ism.ac.jp/~tal/ CONTACT: tal@ism.ac.jp

Algorithms↗
PubMed2002

Practical approaches to analyzing results of microarray experiments.

Microarray technology is rapidly becoming a standard laboratory technique. The main challenges related to the successful implementation of the technology are analysis-related. In 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 methods and outline our approach to using them. We discuss methods for scoring genes for their relevance, focusing on the statistical meaning of microarray results, especially with regard to the problem of multiple testing. We also deal with the problem of adding biologic meaning to the results of microarray experiments and describe advanced tools that represent different but valid directions in providing automated solutions to this problem. The tools and approaches described and discussed here should provide the reader with a preliminary understanding of the analysis of the results of microarray experiments. The practical focus of this review should remove the mystery behind the analysis of microarray experiments, thus leading to more productive and efficient use of the technology.

Fibroblasts↗