Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm

One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the...

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Vydáno v:Entropy (Basel, Switzerland) Ročník 24; číslo 5; s. 693
Hlavní autoři: Yan, Yan, Jiang, Feng, Zhang, Xinan, Tian, Tianhai
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 13.05.2022
MDPI
Témata:
Algorithms
Boolean
complex network
Consistency
Datasets
Gene expression
Genes
graphic model
Information theory
Kinases
Machine learning
molecular regulation
Networks
path consistency
Proteins
Random variables
Statistical inference
Statistical methods
System effectiveness
complex network
molecular regulation
statistical inference
path consistency
graphic model
ISSN:1099-4300, 1099-4300
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Shrnutí:One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the inferred network may depend on the variable order in the dataset. In this work, we develop a new algorithm, called the statistical path-consistency algorithm (SPCA), to solve the problem of the dependence of variable order. This method generates a number of different variable orders using random samples, and then infers a network by using the path-consistent algorithm based on each variable order. We propose measures to determine the edge weights using the corresponding edge weights in the inferred networks, and choose the edges with the largest weights as the putative regulations between genes or proteins. The developed method is rigorously assessed by the six benchmark networks in DREAM challenges, the mitogen-activated protein (MAP) kinase pathway, and a cancer-specific gene regulatory network. The inferred networks are compared with those obtained by using two up-to-date inference methods. The accuracy of the inferred networks shows that the developed method is effective for discovering molecular regulatory systems.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e24050693