Reconstructing gene regulatory networks from homozygous and heterozygous deletion data using Gaussian noise model

Faridah Hani Mohamed Salleh, Shereena Mohd Arif, Suhaila Zainuddin, Mohd Firdaus Raih

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

Abstract

A transcription network is a large and complex network consisting of interacting elements that, over time, affect each other's state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, many research studies have been conducted for reconstructing gene regulatory networks (GRN). In this research study, we propose an algorithm for inferring the regulatory interactions from homozygous and heterozygous deletion data using a Gaussian model. Using simulated gene expression data on networks of known connectivity, we investigate the ability of the proposed algorithm to predict the presence of regulatory interactions between genes and the signed edges (activation or suppression). The algorithm is applied to network sizes of 10 genes and 50 genes for two E.coli subgroups and three S.cerevisiae/Yeast subgroups. The predicted networks were evaluated on the basis of two scoring metrics, area under the ROC curve (AUROC) and area under the precision-recall curve (AUPR). The algorithm has reconstructed the networks with a reasonably low error rate. Our AUPR and AUROC values are consistently higher than the other method compared in this study. The Gaussian model distinguishes real signals from random fluctuations using an iterative method. The analysis of the experiment results reveals that our method can reconstruct networks and predict signed edges with a wide range of network types, connectivity, and noise levels with a reasonable error rate.

Original languageEnglish
Title of host publicationProceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013
EditorsIsmail Saad, Khairul Anuar Mohamad, David Al-Dabass, Mohd Hanafi Ahmad Hijazi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages74-80
Number of pages7
ISBN (Electronic)9781479932511
DOIs
Publication statusPublished - 14 Nov 2014
Event1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013 - Kota Kinabalu, Sabah, Malaysia
Duration: 03 Dec 201305 Dec 2013

Publication series

NameProceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013

Other

Other1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013
CountryMalaysia
CityKota Kinabalu, Sabah
Period03/12/1305/12/13

Fingerprint

Gene Regulatory Network
Gaussian Noise
Deletion
Genes
Gene
Receiver Operating Characteristic Curve
Gaussian Model
Signed
Error Rate
Model
Connectivity
Complex networks
Transcription
Subgroup
Iterative methods
Gene expression
Yeast
Escherichia coli
Predict
Curve

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modelling and Simulation

Cite this

Mohamed Salleh, F. H., Arif, S. M., Zainuddin, S., & Raih, M. F. (2014). Reconstructing gene regulatory networks from homozygous and heterozygous deletion data using Gaussian noise model. In I. Saad, K. A. Mohamad, D. Al-Dabass, & M. H. Ahmad Hijazi (Eds.), Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013 (pp. 74-80). [6959897] (Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIMS.2013.19
Mohamed Salleh, Faridah Hani ; Arif, Shereena Mohd ; Zainuddin, Suhaila ; Raih, Mohd Firdaus. / Reconstructing gene regulatory networks from homozygous and heterozygous deletion data using Gaussian noise model. Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013. editor / Ismail Saad ; Khairul Anuar Mohamad ; David Al-Dabass ; Mohd Hanafi Ahmad Hijazi. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 74-80 (Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013).
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Mohamed Salleh, FH, Arif, SM, Zainuddin, S & Raih, MF 2014, Reconstructing gene regulatory networks from homozygous and heterozygous deletion data using Gaussian noise model. in I Saad, KA Mohamad, D Al-Dabass & MH Ahmad Hijazi (eds), Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013., 6959897, Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013, Institute of Electrical and Electronics Engineers Inc., pp. 74-80, 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013, Kota Kinabalu, Sabah, Malaysia, 03/12/13. https://doi.org/10.1109/AIMS.2013.19

Reconstructing gene regulatory networks from homozygous and heterozygous deletion data using Gaussian noise model. / Mohamed Salleh, Faridah Hani; Arif, Shereena Mohd; Zainuddin, Suhaila; Raih, Mohd Firdaus.

Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013. ed. / Ismail Saad; Khairul Anuar Mohamad; David Al-Dabass; Mohd Hanafi Ahmad Hijazi. Institute of Electrical and Electronics Engineers Inc., 2014. p. 74-80 6959897 (Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013).

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

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T1 - Reconstructing gene regulatory networks from homozygous and heterozygous deletion data using Gaussian noise model

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Mohamed Salleh FH, Arif SM, Zainuddin S, Raih MF. Reconstructing gene regulatory networks from homozygous and heterozygous deletion data using Gaussian noise model. In Saad I, Mohamad KA, Al-Dabass D, Ahmad Hijazi MH, editors, Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013. Institute of Electrical and Electronics Engineers Inc. 2014. p. 74-80. 6959897. (Proceedings - 1st International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2013). https://doi.org/10.1109/AIMS.2013.19