Bioinformatics Vol. 19 Suppl. 1 2003
Pages i264-i272
© 2003 Oxford University Press
Discovering molecular pathways from protein interaction and gene expression data
Computer Science Department, Stanford University, Stanford, CA 94305-9010, USA
Received on January 6, 2003
; accepted on February 20, 2003
In this paper, we describe an approach for identifying ‘pathways’ from gene expression and protein interaction data. Our approach is based on the assumption that many pathways exhibit two properties: their genes exhibit a similar gene expression profile, and the protein products of the genes often interact. Our approach is based on a unified probabilistic model, which is learned from the data using the EM algorithm. We present results on two Saccharomyces cerevisiae gene expression data sets, combined with a binary protein interaction data set. Our results show that our approach is much more successful than other approaches at discovering both coherent functional groups and entire protein complexes.
Contact: eran{at}cs.stanford.edu
Keywords: probabilistic models, protein interaction, gene expression.
* To whom correspondence should be addressed.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  T. Zeng and J. Li Maximization of negative correlations in time-course gene expression data for enhancing understanding of molecular pathways Nucleic Acids Res., October 23, 2009; (2009) gkp822v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Gersten, M. Alirezaei, M. C. G. Marcondes, C. Flynn, T. Ravasi, T. Ideker, and H. S. Fox An Integrated Systems Analysis Implicates EGR1 Downregulation in Simian Immunodeficiency Virus Encephalitis-Induced Neural Dysfunction J. Neurosci., October 7, 2009; 29(40): 12467 - 12476. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Keerthikumar, S. Bhadra, K. Kandasamy, R. Raju, Y. L. Ramachandra, C. Bhattacharyya, K. Imai, O. Ohara, S. Mohan, and A. Pandey Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach DNA Res, October 3, 2009; (2009) dsp019v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 I. Ulitsky and R. Shamir Identifying functional modules using expression profiles and confidence-scored protein interactions Bioinformatics, May 1, 2009; 25(9): 1158 - 1164. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. Georgii, S. Dietmann, T. Uno, P. Pagel, and K. Tsuda Enumeration of condition-dependent dense modules in protein interaction networks Bioinformatics, April 1, 2009; 25(7): 933 - 940. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T.-t. Soong, K. O. Wrzeszczynski, and B. Rost Physical protein-protein interactions predicted from microarrays Bioinformatics, November 15, 2008; 24(22): 2608 - 2614. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Zheng, J. Sheng, C. Wang, X. Wang, Y. Yu, Y. Li, A. Michie, J. Dai, Y. Zhong, P. Hao, et al. MPSQ: a web tool for protein-state searching Bioinformatics, October 15, 2008; 24(20): 2412 - 2413. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Dotan-Cohen, A. A. Melkman, and S. Kasif Hierarchical tree snipping: clustering guided by prior knowledge Bioinformatics, December 15, 2007; 23(24): 3335 - 3342. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. C. Tseng Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data Bioinformatics, September 1, 2007; 23(17): 2247 - 2255. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. Guo, Y. Li, X. Gong, C. Yao, W. Ma, D. Wang, Y. Li, J. Zhu, M. Zhang, D. Yang, et al. Edge-based scoring and searching method for identifying condition-responsive protein protein interaction sub-network Bioinformatics, August 15, 2007; 23(16): 2121 - 2128. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. Hu, D. M. Ng, T. Yamada, C. Chen, S. Kawashima, J. Mellor, B. Linghu, M. Kanehisa, J. M. Stuart, and C. DeLisi VisANT 3.0: new modules for pathway visualization, editing, prediction and construction Nucleic Acids Res., July 13, 2007; 35(suppl_2): W625 - W632. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Huang and T. W. S. Chow Identifying the biologically relevant gene categories based on gene expression and biological data: an example on prostate cancer Bioinformatics, June 15, 2007; 23(12): 1503 - 1510. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. W. Mount and R. Pandey Using bioinformatics and genome analysis for new therapeutic interventions Mol. Cancer Ther., October 1, 2005; 4(10): 1636 - 1643. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. H. Lee and D. Lee Modularized learning of genetic interaction networks from biological annotations and mRNA expression data Bioinformatics, June 1, 2005; 21(11): 2739 - 2747. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Bulashevska and R. Eils Inferring genetic regulatory logic from expression data Bioinformatics, June 1, 2005; 21(11): 2706 - 2713. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. R. Pinto, L. A. Cowart, Y. A. Hannun, B. Rohrer, and J. S. Almeida Local correlation of expression profiles with gene annotations--proof of concept for a general conciliatory method Bioinformatics, April 1, 2005; 21(7): 1037 - 1045. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Ekins, E. Kirillov, E. A. Rakhmatulin, and T. Nikolskaya A NOVEL METHOD FOR VISUALIZING NUCLEAR HORMONE RECEPTOR NETWORKS RELEVANT TO DRUG METABOLISM Drug Metab. Dispos., March 1, 2005; 33(3): 474 - 481. [Abstract] [Full Text] [PDF]
|
|