A novel application of mixing coefficients for reverse-engineering gene interaction networks

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

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

1 Citation (Scopus)

Abstract

In this paper, we present a new application of the so-called phi-mixing coefficient between two random variables. Using the phi-mixing coefficient, as well as an analog of the well-known data processing inequality from information theory, we present a new algorithm for reverse-engineering gene interaction networks (GINs) from expression data, by viewing the expression levels of various genes as coupled random variables. Unlike existing methods, the GINs constructed using the algorithm presented here have edges that are both directed and weighted. Thus it is possible to infer both the direction as well as the strength of the interaction between genes. Several GINs have been constructed for various data sets in lung and ovarian cancer. One of the lung cancer networks is validated by comparing its predictions against the output of ChIP-seq data.

Original languageEnglish (US)
Title of host publication2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012
Pages1461-1466
Number of pages6
DOIs
StatePublished - 2012
Event2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012 - Monticello, IL, United States
Duration: Oct 1 2012Oct 5 2012

Other

Other2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012
CountryUnited States
CityMonticello, IL
Period10/1/1210/5/12

Fingerprint

Reverse engineering
Genes
Random variables
Information theory

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Singh, N., Ahsen, M. E., Mankala, S., Vidyasagar, M., & White, M. A. (2012). A novel application of mixing coefficients for reverse-engineering gene interaction networks. In 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012 (pp. 1461-1466). [6483391] https://doi.org/10.1109/Allerton.2012.6483391

A novel application of mixing coefficients for reverse-engineering gene interaction networks. / Singh, Nitin; Ahsen, M. Eren; Mankala, Shiva; Vidyasagar, M.; White, Michael A.

2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012. 2012. p. 1461-1466 6483391.

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

Singh, N, Ahsen, ME, Mankala, S, Vidyasagar, M & White, MA 2012, A novel application of mixing coefficients for reverse-engineering gene interaction networks. in 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012., 6483391, pp. 1461-1466, 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012, Monticello, IL, United States, 10/1/12. https://doi.org/10.1109/Allerton.2012.6483391
Singh N, Ahsen ME, Mankala S, Vidyasagar M, White MA. A novel application of mixing coefficients for reverse-engineering gene interaction networks. In 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012. 2012. p. 1461-1466. 6483391 https://doi.org/10.1109/Allerton.2012.6483391
Singh, Nitin ; Ahsen, M. Eren ; Mankala, Shiva ; Vidyasagar, M. ; White, Michael A. / A novel application of mixing coefficients for reverse-engineering gene interaction networks. 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012. 2012. pp. 1461-1466
@inproceedings{ad5e5e47bb9a4232afd17642cdcb83e7,
title = "A novel application of mixing coefficients for reverse-engineering gene interaction networks",
abstract = "In this paper, we present a new application of the so-called phi-mixing coefficient between two random variables. Using the phi-mixing coefficient, as well as an analog of the well-known data processing inequality from information theory, we present a new algorithm for reverse-engineering gene interaction networks (GINs) from expression data, by viewing the expression levels of various genes as coupled random variables. Unlike existing methods, the GINs constructed using the algorithm presented here have edges that are both directed and weighted. Thus it is possible to infer both the direction as well as the strength of the interaction between genes. Several GINs have been constructed for various data sets in lung and ovarian cancer. One of the lung cancer networks is validated by comparing its predictions against the output of ChIP-seq data.",
author = "Nitin Singh and Ahsen, {M. Eren} and Shiva Mankala and M. Vidyasagar and White, {Michael A.}",
year = "2012",
doi = "10.1109/Allerton.2012.6483391",
language = "English (US)",
isbn = "9781467345385",
pages = "1461--1466",
booktitle = "2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012",

}

TY - GEN

T1 - A novel application of mixing coefficients for reverse-engineering gene interaction networks

AU - Singh, Nitin

AU - Ahsen, M. Eren

AU - Mankala, Shiva

AU - Vidyasagar, M.

AU - White, Michael A.

PY - 2012

Y1 - 2012

N2 - In this paper, we present a new application of the so-called phi-mixing coefficient between two random variables. Using the phi-mixing coefficient, as well as an analog of the well-known data processing inequality from information theory, we present a new algorithm for reverse-engineering gene interaction networks (GINs) from expression data, by viewing the expression levels of various genes as coupled random variables. Unlike existing methods, the GINs constructed using the algorithm presented here have edges that are both directed and weighted. Thus it is possible to infer both the direction as well as the strength of the interaction between genes. Several GINs have been constructed for various data sets in lung and ovarian cancer. One of the lung cancer networks is validated by comparing its predictions against the output of ChIP-seq data.

AB - In this paper, we present a new application of the so-called phi-mixing coefficient between two random variables. Using the phi-mixing coefficient, as well as an analog of the well-known data processing inequality from information theory, we present a new algorithm for reverse-engineering gene interaction networks (GINs) from expression data, by viewing the expression levels of various genes as coupled random variables. Unlike existing methods, the GINs constructed using the algorithm presented here have edges that are both directed and weighted. Thus it is possible to infer both the direction as well as the strength of the interaction between genes. Several GINs have been constructed for various data sets in lung and ovarian cancer. One of the lung cancer networks is validated by comparing its predictions against the output of ChIP-seq data.

UR - http://www.scopus.com/inward/record.url?scp=84875733385&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84875733385&partnerID=8YFLogxK

U2 - 10.1109/Allerton.2012.6483391

DO - 10.1109/Allerton.2012.6483391

M3 - Conference contribution

AN - SCOPUS:84875733385

SN - 9781467345385

SP - 1461

EP - 1466

BT - 2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012

ER -