Ziv Bar-Joseph

Summary

Affiliation: Carnegie Mellon University
Country: USA

Publications

  1. pmc Reconstructing dynamic regulatory maps
    Jason Ernst
    Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Mol Syst Biol 3:74. 2007
  2. pmc Predicting tissue specific transcription factor binding sites
    Shan Zhong
    Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
    BMC Genomics 14:796. 2013
  3. doi request reprint Studying and modelling dynamic biological processes using time-series gene expression data
    Ziv Bar-Joseph
    Lane Center for Computational Biology and Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
    Nat Rev Genet 13:552-64. 2012
  4. pmc Impact of the solvent capacity constraint on E. coli metabolism
    Alexei Vazquez
    The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ 08540, USA
    BMC Syst Biol 2:7. 2008
  5. pmc Combination of genomic approaches with functional genetic experiments reveals two modes of repression of yeast middle-phase meiosis genes
    Michael Klutstein
    Department of Microbiology and Molecular Genetics, The Institute for Medical Research Israel Canada, The Hebrew University Hadassah Medical School, Jerusalem, Israel
    BMC Genomics 11:478. 2010
  6. pmc Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells
    Ziv Bar-Joseph
    Department of Computer Science, School of Computer Science and Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Proc Natl Acad Sci U S A 105:955-60. 2008
  7. ncbi request reprint Deconvolving cell cycle expression data with complementary information
    Ziv Bar-Joseph
    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 20:i23-30. 2004
  8. ncbi request reprint Analyzing time series gene expression data
    Ziv Bar-Joseph
    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15217, USA
    Bioinformatics 20:2493-503. 2004
  9. ncbi request reprint Clustering short time series gene expression data
    Jason Ernst
    Center for Automated Learning and Discovery, School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
    Bioinformatics 21:i159-68. 2005
  10. pmc A probabilistic generative model for GO enrichment analysis
    Yong Lu
    Computer Science Department, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213 USA
    Nucleic Acids Res 36:e109. 2008

Detail Information

Publications65

  1. pmc Reconstructing dynamic regulatory maps
    Jason Ernst
    Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Mol Syst Biol 3:74. 2007
    ..The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation...
  2. pmc Predicting tissue specific transcription factor binding sites
    Shan Zhong
    Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
    BMC Genomics 14:796. 2013
    ....
  3. doi request reprint Studying and modelling dynamic biological processes using time-series gene expression data
    Ziv Bar-Joseph
    Lane Center for Computational Biology and Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
    Nat Rev Genet 13:552-64. 2012
    ....
  4. pmc Impact of the solvent capacity constraint on E. coli metabolism
    Alexei Vazquez
    The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ 08540, USA
    BMC Syst Biol 2:7. 2008
    ..Molecular crowding in a cell's cytoplasm is one such potential constraint, as it limits the solvent capacity available to metabolic enzymes...
  5. pmc Combination of genomic approaches with functional genetic experiments reveals two modes of repression of yeast middle-phase meiosis genes
    Michael Klutstein
    Department of Microbiology and Molecular Genetics, The Institute for Medical Research Israel Canada, The Hebrew University Hadassah Medical School, Jerusalem, Israel
    BMC Genomics 11:478. 2010
    ..It has been suggested that the competition between Ndt80 and Sum1 determines the temporal expression of their targets during middle meiosis...
  6. pmc Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells
    Ziv Bar-Joseph
    Department of Computer Science, School of Computer Science and Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Proc Natl Acad Sci U S A 105:955-60. 2008
    ..This conclusion was supported by both bioinformatic analysis and experiments performed on other cell types. We suggest that this approach will help pinpoint genetic elements contributing to normal cell growth and cellular transformation...
  7. ncbi request reprint Deconvolving cell cycle expression data with complementary information
    Ziv Bar-Joseph
    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 20:i23-30. 2004
    ..Deconvolving the observed values from a mixed population will allow us to obtain better models for these systems and to accurately detect the genes that participate in these systems...
  8. ncbi request reprint Analyzing time series gene expression data
    Ziv Bar-Joseph
    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15217, USA
    Bioinformatics 20:2493-503. 2004
    ..g. handling the different non-uniform sampling rates)...
  9. ncbi request reprint Clustering short time series gene expression data
    Jason Ernst
    Center for Automated Learning and Discovery, School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
    Bioinformatics 21:i159-68. 2005
    ..Most clustering algorithms are unable to distinguish between real and random patterns...
  10. pmc A probabilistic generative model for GO enrichment analysis
    Yong Lu
    Computer Science Department, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213 USA
    Nucleic Acids Res 36:e109. 2008
    ..When used with microarray expression data and ChIP-chip data from yeast and human our method was able to correctly identify both general and specific enriched categories which were overlooked by other methods...
  11. ncbi request reprint Continuous hidden process model for time series expression experiments
    Yanxin Shi
    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 23:i459-67. 2007
    ....
  12. pmc Learning cellular sorting pathways using protein interactions and sequence motifs
    Tien ho Lin
    Language Technology Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
    J Comput Biol 18:1709-22. 2011
    ..Supplementary results and software implementation are available from http://murphylab.web.cmu.edu/software/2010_RECOMB_pathways/...
  13. pmc Cross species expression analysis of innate immune response
    Yong Lu
    School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    J Comput Biol 17:253-68. 2010
    ..Our results shed light on immune response mechanisms and on the differences between various types of cells that are used to fight infecting bacteria. For supporting website, see www.cs.cmu.edu/-lyongu/pub/immune/...
  14. doi request reprint Extracting dynamics from static cancer expression data
    Anupam Gupta
    Department of Computer Science, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    IEEE/ACM Trans Comput Biol Bioinform 5:172-82. 2008
    ..A classifier that utilizes this ordering improves upon other classifiers suggested for this task. The set of genes displaying consistent behavior for the determined ordering are enriched for genes associated with cancer progression...
  15. pmc MicroRNA regulation and its effects on cellular transcriptome in human immunodeficiency virus-1 (HIV-1) infected individuals with distinct viral load and CD4 cell counts
    Karolina Duskova
    Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, 425 Parran Hall, 130 DeSoto Street, Pittsburgh, PA 15261, USA
    BMC Infect Dis 13:250. 2013
    ....
  16. ncbi request reprint Inferring pairwise regulatory relationships from multiple time series datasets
    Yanxin Shi
    Machine Learning Department, Language Technologies Institute, Computer Science Department and Department of Biological Sciences, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA
    Bioinformatics 23:755-63. 2007
    ....
  17. pmc Integrating multiple evidence sources to predict transcription factor binding in the human genome
    Jason Ernst
    Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Genome Res 20:526-36. 2010
    ..When combined with motif information our method outperforms previous methods for predicting locations of true binding...
  18. pmc Backup in gene regulatory networks explains differences between binding and knockout results
    Anthony Gitter
    Computer Science Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Mol Syst Biol 5:276. 2009
    ..New double knockout experiments support our conclusions. Our results highlight the robustness provided by redundant TFs and indicate that in the context of diverse cellular systems, binding is still largely functional...
  19. pmc STEM: a tool for the analysis of short time series gene expression data
    Jason Ernst
    Center for Automated and Learning and Discovery, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    BMC Bioinformatics 7:191. 2006
    ..Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data...
  20. pmc Discovering pathways by orienting edges in protein interaction networks
    Anthony Gitter
    Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA
    Nucleic Acids Res 39:e22. 2011
    ..For some pathways, including the pheromone signaling pathway and the high-osmolarity glycerol pathway, our method suggests interesting and novel components that extend current annotations...
  21. ncbi request reprint Identifying cycling genes by combining sequence homology and expression data
    Yong Lu
    School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
    Bioinformatics 22:e314-22. 2006
    ..Due to noise and other data analysis problems, accurately deriving a set of cycling genes from expression data is a hard problem. This is especially true for some of the multicellular organisms, including humans...
  22. pmc Matching experiments across species using expression values and textual information
    Aaron Wise
    Lane Center for Computational Biology, Carnegie Mellon University Pittsburgh, PA 15213, USA
    Bioinformatics 28:i258-64. 2012
    ..Unlike sequence, which is static, expression data changes over time and under different conditions. Thus, a prerequisite for performing cross-species analysis is the ability to match experiments across species...
  23. pmc Combined analysis reveals a core set of cycling genes
    Yong Lu
    Department of Computer Science, Carnegie Mellon University, Forbes Avenue, Pittsburgh, Pennsylvania 15213, USA
    Genome Biol 8:R146. 2007
    ..Combining and comparing data from multiple species is challenging because of noise in expression data, the different synchronization and scoring methods used, and the need to determine an accurate set of homologs...
  24. pmc A combined expression-interaction model for inferring the temporal activity of transcription factors
    Yanxin Shi
    Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    J Comput Biol 16:1035-49. 2009
    ..Using real data, we also show that PTMM can recover meaningful TF activity levels and identify post-transcriptionally modified TFs, many of which are supported by other sources. Supporting website: www.sb.cs.cmu.edu/PTMM/PTMM.html...
  25. pmc A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli
    Jason Ernst
    Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
    PLoS Comput Biol 4:e1000044. 2008
    ....
  26. pmc Carbon catabolite repression correlates with the maintenance of near invariant molecular crowding in proliferating E. coli cells
    Yi Zhou
    Department of Pathology, University of Pittsburgh, School of Medicine, S701 Scaife Hall, 3550 Terrace Street, Pittsburgh, PA 15213, USA
    BMC Syst Biol 7:138. 2013
    ....
  27. pmc Protein complex identification by supervised graph local clustering
    Yanjun Qi
    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 24:i250-8. 2008
    ..While this assumption is true for some complexes, it does not hold for many others. New algorithms are required in order to recover complexes with other types of topological structure...
  28. pmc Identifying proteins controlling key disease signaling pathways
    Anthony Gitter
    Computer Science Department and Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
    Bioinformatics 29:i227-36. 2013
    ..Such mechanistic models could be used to accurately predict downstream effects of knocking down pathway members and allow comprehensive exploration of the effects of targeting pairs or higher-order combinations of genes...
  29. pmc DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data
    Marcel H Schulz
    Ray and Stephanie Lane Center for Computational Biology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
    BMC Syst Biol 6:104. 2012
    ..DREM has been used successfully in diverse areas of biological research. However, several issues were not addressed by the original version...
  30. pmc Biological interaction networks are conserved at the module level
    Guy E Zinman
    Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    BMC Syst Biol 5:134. 2011
    ..Several recent studies comparing high throughput data including expression, protein-protein, protein-DNA, and genetic interactions between close species show conservation at a much lower rate than expected...
  31. pmc Cross-species queries of large gene expression databases
    Hai Son Le
    Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
    Bioinformatics 26:2416-23. 2010
    ..Thus, to carry out cross-species analysis using these databases, we need methods that can match experiments in one species with experiments in another species...
  32. ncbi request reprint A patient-gene model for temporal expression profiles in clinical studies
    Naftali Kaminski
    Simmons Center for Interstitial Lung Disease, University of Pittsburgh Medical School, Pittsburgh, Pennsylvania, USA
    J Comput Biol 14:324-38. 2007
    ..As we show, our algorithm was able to improve upon prior methods for combining patients data. In addition, our algorithm was able to correctly identify patient specific response patterns...
  33. pmc Integrating sequence, expression and interaction data to determine condition-specific miRNA regulation
    Hai Son Le
    Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 29:i89-97. 2013
    ..Early work on miRNA target prediction has focused on using static sequence information. More recently, researchers have combined sequence and expression data to identify such targets in various conditions...
  34. pmc A network-based approach for predicting missing pathway interactions
    Saket Navlakha
    School of Computer Science and Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
    PLoS Comput Biol 8:e1002640. 2012
    ..We also performed experiments that support a novel interaction not previously reported. Our framework is general and may be applicable to edge prediction problems in other domains...
  35. pmc Reconstructing dynamic microRNA-regulated interaction networks
    Marcel H Schulz
    Ray and Stephanie Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213
    Proc Natl Acad Sci U S A 110:15686-91. 2013
    ..These results indicate that some disease progression pathways in idiopathic pulmonary fibrosis may represent partial reversal of lung differentiation. ..
  36. pmc Linking the signaling cascades and dynamic regulatory networks controlling stress responses
    Anthony Gitter
    Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    Genome Res 23:365-76. 2013
    ..Consequently, our method is widely applicable and can be used to derive accurate, dynamic response models in several species...
  37. pmc A mixture of feature experts approach for protein-protein interaction prediction
    Yanjun Qi
    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    BMC Bioinformatics 8:S6. 2007
    ..In addition, in many cases it is hard to directly determine which of the data sources contributed to a prediction. This information is important for biologists using these predications in the design of new experiments...
  38. doi request reprint Computational methods for analyzing dynamic regulatory networks
    Anthony Gitter
    Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA
    Methods Mol Biol 674:419-41. 2010
    ..We point to software tools implementing the methods discussed in this chapter. As more temporal measurements become available, the importance of analyzing such data and of combining it with other types of data will greatly increase...
  39. pmc Discriminative motif finding for predicting protein subcellular localization
    Tien ho Lin
    Carnegie Mellon University, Pittsburgh, Pittsburgh, PA 15213, USA
    IEEE/ACM Trans Comput Biol Bioinform 8:441-51. 2011
    ..A software implementation and the data set described in this paper are available from http://murphylab.web.cmu.edu/software/2009_TCBB_motif/...
  40. doi request reprint Transcriptome analyses identify key cellular factors associated with HIV-1 associated neuropathogenesis in infected men
    Narasimhan J Venkatachari
    aDepartment of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261 bComputer Science Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15217, USA cMolecular Biology Information Service, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA dComputational Biology and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA eDepartment of Radiology, Feinberg School of Medicine, Northwestern University, Suite 1600, 737 N Michigan Ave, Chicago, IL 60611, USA fDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21209, USA gDepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, CA 90095 hDepartment of Psychiatry, Rush University Medical Center, 1645 W Jackson Blvd, Chicago, IL, 60612, USA iDepartment of Neurology, Johns Hopkins Bayview Medical Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21209, USA
    AIDS . 2016
    ..We applied novel Systems Biology approaches to identify specific cellular and viral factors and their related pathways that are associated with different stages of HAND...
  41. pmc Alignment and classification of time series gene expression in clinical studies
    Tien ho Lin
    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 24:i147-55. 2008
    ..Methods that are specifically designed for this temporal data can both utilize its unique features (temporal evolution of profiles) and address its unique challenges (different response rates of patients in the same class)...
  42. pmc DECOD: fast and accurate discriminative DNA motif finding
    Peter Huggins
    Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 27:2361-7. 2011
    ..Another issue is identifying discriminative rather than generative motifs. Such discriminative motifs are important for identifying co-factors and for explaining changes in behavior between different conditions...
  43. pmc Algorithms in nature: the convergence of systems biology and computational thinking
    Saket Navlakha
    Lane Center for Computational Biology and Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Mol Syst Biol 7:546. 2011
    ..With the rapid accumulation of data detailing the inner workings of biological systems, we expect this direction of coupling biological and computational studies to greatly expand in the future...
  44. pmc Comparative expression profile of miRNA and mRNA in primary peripheral blood mononuclear cells infected with human immunodeficiency virus (HIV-1)
    Ankit Gupta
    Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
    PLoS ONE 6:e22730. 2011
    ..These results for the first time provide evidence that the miRNA profile could be an early indicator of host cellular dysfunction induced by HIV-1...
  45. pmc Cross species analysis of microarray expression data
    Yong Lu
    School of Computer Science and Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 25:1476-83. 2009
    ....
  46. pmc Reconstructing the temporal progression of HIV-1 immune response pathways
    Siddhartha Jain
    Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA
    Bioinformatics 32:i253-i261. 2016
    ..Most methods for reconstructing response networks from high throughput data generate static models which cannot distinguish between early and late response stages...
  47. pmc Probabilistic error correction for RNA sequencing
    Hai Son Le
    Machine Learning Department, Carnegie Mellon University, 5000 Forbes Avenue Pittsburgh, PA 15217, USA
    Nucleic Acids Res 41:e109. 2013
    ..Supporting website: http://sb.cs.cmu.edu/seecer/...
  48. pmc Systematic prediction of human membrane receptor interactions
    Yanjun Qi
    School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
    Proteomics 9:5243-55. 2009
    ..The results suggest that a framework of systematically integrating computational predictions, global analyses, biological experimentation and expert feedback is a feasible strategy to study the human membrane receptor interactome...
  49. pmc Topological properties of robust biological and computational networks
    Saket Navlakha
    Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    J R Soc Interface 11:20140283. 2014
    ..Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields. ..
  50. pmc A high-throughput framework to detect synapses in electron microscopy images
    Saket Navlakha
    Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 29:i9-17. 2013
    ..g. electrophysiology), prone to error and difficult to automate (e.g. standard electron microscopy) or too coarse (e.g. magnetic resonance imaging) to provide accurate and large-scale measurements...
  51. pmc Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments
    Emre Sefer
    Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
    Cell Syst 3:35-42. 2016
    ..These results provide theoretical support to the large number of high-throughput time series experiments that do not use replicates. ..
  52. doi request reprint Selecting the most appropriate time points to profile in high-throughput studies
    Michael Kleyman
    Machine Learning and Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States
    elife 6:. 2017
    ..i>TPS can thus serve as a key design strategy for high throughput time series experiments...
  53. pmc Temporal transcriptional response to latency reversing agents identifies specific factors regulating HIV-1 viral transcriptional switch
    Narasimhan J Venkatachari
    Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh GSPH, Room A435, Crabtree Hall, 130 DeSoto Street, Pittsburgh, PA, 15261, USA
    Retrovirology 12:85. 2015
    ..This underlines the critical need to develop innovative strategies to predict and recognize ways that could result in better reactivation and eventual elimination of latent HIV-1 reservoirs...
  54. pmc ModuleBlast: identifying activated sub-networks within and across species
    Guy E Zinman
    Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Nucleic Acids Res 43:e20. 2015
    ..We have experimentally validated some of the novel hypotheses resulting from the analysis of the ModuleBlast results leading to new insights into the mechanisms used by a key mammalian aging protein. ..
  55. pmc cDREM: inferring dynamic combinatorial gene regulation
    Aaron Wise
    Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania
    J Comput Biol 22:324-33. 2015
    ..Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs. ..
  56. pmc SMARTS: reconstructing disease response networks from multiple individuals using time series gene expression data
    Aaron Wise
    Lane Center for Computational Biology and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
    Bioinformatics 31:1250-7. 2015
    ..Thus, new methods are required to integrate time series data from multiple individuals when modeling and constructing disease response networks...
  57. pmc Multitask learning of signaling and regulatory networks with application to studying human response to flu
    Siddhartha Jain
    Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
    PLoS Comput Biol 10:e1003943. 2014
    ..Supporting website with MT-SDREM implementation: http://sb.cs.cmu.edu/mtsdrem. ..
  58. pmc Evaluation of different biological data and computational classification methods for use in protein interaction prediction
    Yanjun Qi
    School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    Proteins 63:490-500. 2006
    ..Strikingly, gene expression is consistently the most important feature for all three prediction tasks, while the protein interactions identified using the yeast-2-hybrid system were not among the top-ranking features under any condition...
  59. ncbi request reprint Combined static and dynamic analysis for determining the quality of time-series expression profiles
    Itamar Simon
    Dept Molecular Biology, Hebrew University Medical School, Jerusalem, Israel 91120
    Nat Biotechnol 23:1503-8. 2005
    ..Experimental validation of these results shows the utility of this analytical approach for determining the accuracy of gene expression patterns...
  60. ncbi request reprint Computational discovery of gene modules and regulatory networks
    Ziv Bar-Joseph
    MIT Computer Science and Artificial Intelligence Laboratory, 200 Technology Square, Cambridge, Massachusetts 02139, USA
    Nat Biotechnol 21:1337-42. 2003
    ..We also present a genome-wide location analysis data set for regulators in yeast cells treated with rapamycin, and use the GRAM algorithm to provide biological insights into this regulatory network..
  61. ncbi request reprint Transcriptional regulatory networks in Saccharomyces cerevisiae
    Tong Ihn Lee
    Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, MA 02142, USA
    Science 298:799-804. 2002
    ..Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators...
  62. ncbi request reprint Continuous representations of time-series gene expression data
    Ziv Bar-Joseph
    MIT Laboratory for Computer Science, 200 Technology Square, Cambridge, MA 02139, USA
    J Comput Biol 10:341-56. 2003
    ..We demonstrate that our algorithm produces stable low-error alignments on real expression data and further show a specific application to yeast knock-out data that produces biologically meaningful results...
  63. pmc Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes
    Ziv Bar-Joseph
    Laboratory for Computer Science, Massachusetts Institute of Technology, 200 Technology Square, Cambridge, MA 02139, USA
    Proc Natl Acad Sci U S A 100:10146-51. 2003
    ..This set of genes is significantly correlated in a set of independent expression experiments, suggesting additional roles for the transcription factors Fkh1 and Fkh2 in controlling cellular activity in yeast...
  64. ncbi request reprint K-ary clustering with optimal leaf ordering for gene expression data
    Ziv Bar-Joseph
    Laboratory for Computer Science, MIT, 545 Technology Square, Cambridge, MA 02139, USA
    Bioinformatics 19:1070-8. 2003
    ..In this paper we propose a new hierarchical clustering algorithm which reduces susceptibility to noise, permits up to k siblings to be directly related, and provides a single optimal order for the resulting tree...
  65. pmc A critical assessment of Mus musculus gene function prediction using integrated genomic evidence
    Lourdes Pena-Castillo
    Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S3E1, Canada
    Genome Biol 9:S2. 2008
    ..Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated...