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IPM
30
YEARS OLD

“School of Biological Sciences”

Paper   IPM / Biological Sciences / 14119
   School of Biological Sciences
  Title: Inferring gene regulatory networks by an order independent algorithm using incomplete data sets
  Author(s):
1 . Rosa Aghdam
2 . M. Ganjali.
3 . P. Niloofar.
4 . C. Eslahchi.
  Status: Published
  Journal: J. Appl. Statist.
  Vol.: DOI:10.1080/02664763.2015.1079307
  Year: 2016
  Pages: 1-21
  Editor: Taylor & Francis
  Supported by: IPM
  Abstract:
Analyzing incomplete data for inferring the structure of gene regulatory networks (GRNs) is a challenging task in bioinformatic. Bayesian network can be successfully used in this field. k-nearest neighbor, singular value decomposition (SVD)-based and multiple imputation by chained equations are three fundamental imputation methods to deal with missing values. Path consistency (PC) algorithm based on conditional mutual information (PCA�??CMI) is a famous algorithm for inferring GRNs. This algorithm needs the data set to be complete. However, the problem is that PCA�??CMI is not a stable algorithm and when applied on permuted gene orders, different networks are obtained. We propose an order independent algorithm, PCA�??CMI�??OI, for inferring GRNs. After imputation of missing data, the performances of PCA�??CMI and PCA�??CMI�??OI are compared. Results show that networks constructed from data imputed by the SVD-based method and PCA�??CMI�??OI algorithm outperform other imputation methods and PCA�??CMI. An undirected or partially directed network is resulted by PC-based algorithms. Mutual information test (MIT) score, which can deal with discrete data, is one of the famous methods for directing the edges of resulted networks. We also propose a new score, ConMIT, which is appropriate for analyzing continuous data. Results shows that the precision of directing the edges of skeleton is improved by applying the ConMIT score.

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