Inferring weighted and directed gene interaction networks from gene expression data using the phi-mixing coefficient

Nitin Singh, Mehmet Eren Ahsen, Shiva Mankala, M. Vidyasagar, Michael White

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

2 Citations (Scopus)

Abstract

In this paper, we present a new algorithm for reverse-engineering gene interaction networks (GINs) from expression data, using the so-called phi-mixing coefficient between two random variables. Unlike existing methods, the GINs constructed using the algorithm presented here have edges that are both directed and weighted. The GIN constructed is, in a very specific sense, a minimal network that is compatible with the data. Several GINs have been constructed for various data sets in lung cancer, ovarian cancer and melanoma. Lung cancer and melanoma networks have been validated by comparing their predictions against the output of ChIP-seq data. The neighbors of three transcription factors (ASCL1, PPARG and NKX2-1) in lung cancer, and one transcription factor SOX10 in melanoma, are significantly enriched with ChIP-seq genes compared to pure chance.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
Pages168-171
Number of pages4
DOIs
StatePublished - 2012
Event2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012 - Washington, DC, United States
Duration: Dec 2 2012Dec 4 2012

Other

Other2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012
CountryUnited States
CityWashington, DC
Period12/2/1212/4/12

Fingerprint

Gene Regulatory Networks
Gene expression
Genes
Gene Expression
Melanoma
Lung Neoplasms
SOXE Transcription Factors
Transcription factors
Ovarian Neoplasms
Reverse engineering
Transcription Factors
Random variables

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Computational Theory and Mathematics
  • Signal Processing
  • Biomedical Engineering

Cite this

Singh, N., Ahsen, M. E., Mankala, S., Vidyasagar, M., & White, M. (2012). Inferring weighted and directed gene interaction networks from gene expression data using the phi-mixing coefficient. In Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics (pp. 168-171). [6507755] https://doi.org/10.1109/GENSIPS.2012.6507755

Inferring weighted and directed gene interaction networks from gene expression data using the phi-mixing coefficient. / Singh, Nitin; Ahsen, Mehmet Eren; Mankala, Shiva; Vidyasagar, M.; White, Michael.

Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics. 2012. p. 168-171 6507755.

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

Singh, N, Ahsen, ME, Mankala, S, Vidyasagar, M & White, M 2012, Inferring weighted and directed gene interaction networks from gene expression data using the phi-mixing coefficient. in Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics., 6507755, pp. 168-171, 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012, Washington, DC, United States, 12/2/12. https://doi.org/10.1109/GENSIPS.2012.6507755
Singh N, Ahsen ME, Mankala S, Vidyasagar M, White M. Inferring weighted and directed gene interaction networks from gene expression data using the phi-mixing coefficient. In Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics. 2012. p. 168-171. 6507755 https://doi.org/10.1109/GENSIPS.2012.6507755
Singh, Nitin ; Ahsen, Mehmet Eren ; Mankala, Shiva ; Vidyasagar, M. ; White, Michael. / Inferring weighted and directed gene interaction networks from gene expression data using the phi-mixing coefficient. Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics. 2012. pp. 168-171
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