Inferring Gene Regulatory Networks Via Ensemble Path Consistency Algorithm Based on Conditional Mutual Information

Utilizing gene expression data to infer gene regulatory networks has received great attention because gene regulation networks can reveal complex life phenomena by studying the interaction mechanism among nodes. However, the reconstruction of large-scale gene regulatory networks is often not ideal d...

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Veröffentlicht in:IEEE/ACM transactions on computational biology and bioinformatics Jg. 20; H. 3; S. 1 - 10
Hauptverfasser: Xu, Jie, Yang, Guanxue, Liu, Guohai, Liu, Hui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Biological system modeling
Computational Biology - methods
Consistency
Correlation
Covariance matrices
Entropy
Gene expression
Gene regulation
Gene regulatory network
Gene Regulatory Networks - genetics
Information processing
Mutual information
network reconstruction
Networks
Nodes
Principal Component Analysis
Principal components analysis
Random variables
ISSN:1545-5963, 1557-9964, 1557-9964
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Zusammenfassung:Utilizing gene expression data to infer gene regulatory networks has received great attention because gene regulation networks can reveal complex life phenomena by studying the interaction mechanism among nodes. However, the reconstruction of large-scale gene regulatory networks is often not ideal due to the curse of dimensionality and the impact of external noise. In order to solve this problem, we introduce a novel algorithms called ensemble path consistency algorithm based on conditional mutual information (EPCACMI), whose threshold of mutual information is dynamically self-adjusted. We first use principal component analysis to decompose a large-scale network into several subnetworks. Then, according to the absolute value of coefficient of each principal component, we could remove a large number of unrelated nodes in every subnetwork and infer the relationships among these selected nodes. Finally, all inferred subnetworks are integrated to form the structure of the complete network. Rather than inferring the whole network directly, the influence of a mass of redundant noise could be weakened. Compared with other related algorithms like MRNET, ARACNE, PCAPMI and PCACMI, the results show that EPCACMI is more effective and more robust when inferring gene regulatory networks with more nodes.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2022.3220581