Using knowledge driven matrix factorization to reconstruct modular gene regulatory network

Yang Zhou, Zheng Li, Xuerui Yang, Linxia Zhang, Shireesh Srivastava, Rong Jin, Christina Chan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Reconstructing gene networks from micro-array data can provide information on the mechanisms that govern cellular processes. Numerous studies have been devoted to addressing this problem. A popular method is to view the gene network as a Bayesian inference network, and to apply structure learning methods to determine the topology of the gene network. There are, however, several shortcomings with the Bayesian structure learning approach for reconstructing gene networks. They include high computational cost associated with analyzing a large number of genes and inefficiency in exploiting prior knowledge of co-regulation that could be derived from Gene Ontology (GO) information. In this paper, we present a knowledge driven matrix factorization (KMF) framework for reconstructing modular gene networks that addresses these shortcomings. In KMF, gene expression data is initially used to estimate the correlation matrix. The gene modules and the interactions among the modules are derived by factorizing the correlation matrix. The prior knowledge in GO is integrated into matrix factorization to help identify the gene modules. An alternating optimization algorithm is presented to efficiently find the solution. Experiments show that our algorithm performs significantly better in identifying gene modules than several state-of-the-art algorithms, and the interactions among the modules uncovered by our algorithm are proved to be biologically meaningful.

LanguageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Pages811-816
Number of pages6
Volume2
StatePublished - 2008
Event23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08 - Chicago, IL, United States
Duration: Jul 13 2008Jul 17 2008

Other

Other23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08
CountryUnited States
CityChicago, IL
Period7/13/087/17/08

Profile

Factorization
Genes
Ontology
Gene expression
Topology

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Zhou, Y., Li, Z., Yang, X., Zhang, L., Srivastava, S., Jin, R., & Chan, C. (2008). Using knowledge driven matrix factorization to reconstruct modular gene regulatory network. In Proceedings of the National Conference on Artificial Intelligence (Vol. 2, pp. 811-816)

Using knowledge driven matrix factorization to reconstruct modular gene regulatory network. / Zhou, Yang; Li, Zheng; Yang, Xuerui; Zhang, Linxia; Srivastava, Shireesh; Jin, Rong; Chan, Christina.

Proceedings of the National Conference on Artificial Intelligence. Vol. 2 2008. p. 811-816.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhou, Y, Li, Z, Yang, X, Zhang, L, Srivastava, S, Jin, R & Chan, C 2008, Using knowledge driven matrix factorization to reconstruct modular gene regulatory network. in Proceedings of the National Conference on Artificial Intelligence. vol. 2, pp. 811-816, 23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08, Chicago, IL, United States, 7/13/08.
Zhou Y, Li Z, Yang X, Zhang L, Srivastava S, Jin R et al. Using knowledge driven matrix factorization to reconstruct modular gene regulatory network. In Proceedings of the National Conference on Artificial Intelligence. Vol. 2. 2008. p. 811-816.
Zhou, Yang ; Li, Zheng ; Yang, Xuerui ; Zhang, Linxia ; Srivastava, Shireesh ; Jin, Rong ; Chan, Christina. / Using knowledge driven matrix factorization to reconstruct modular gene regulatory network. Proceedings of the National Conference on Artificial Intelligence. Vol. 2 2008. pp. 811-816
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