Inferring gene regulatory networks by an order independent algorithm using incomplete data sets

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 thre...

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Vydáno v:Journal of applied statistics Ročník 43; číslo 5; s. 893 - 913
Hlavní autoři: Aghdam, Rosa, Ganjali, Mojtaba, Niloofar, Parisa, Eslahchi, Changiz
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 03.04.2016
Taylor & Francis Ltd
Témata:
Algorithms
Bayesian analysis
Bayesian networks
Bioinformatics
Consistency
Genes
Government regulations
inferring the structure of gene regulatory networks
Mathematical analysis
Mean square errors
Methods
missing value imputation
MIT score
mutual information test
Networks
PC algorithm
Tasks
ISSN:0266-4763, 1360-0532
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Shrnutí: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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ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2015.1079307