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Bioengineering (Basel)

Title:		Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm
Author(s):		Zhai J; Ji J; Liu J;
Address:		"Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"
Journal Title:		Bioengineering (Basel)
Year:		2023
Volume:		20230731
Issue:		8
Page Number:		-
DOI:		10.3390/bioengineering10080909
ISSN/ISBN:		2306-5354 (Print) 2306-5354 (Electronic) 2306-5354 (Linking)
Abstract:		"A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data"
Keywords:		CBNs fusion causal biological networks causal brain networks causal protein signaling networks parallel ant colony optimization pheromone fusion;
Notes:		"PubMed-not-MEDLINEZhai, Jihao Ji, Junzhong Liu, Jinduo eng 62106009/National Natural Science Foundation of China/ 62276010/National Natural Science Foundation of China/ KM202210005030/R&D Program of Beijing Municipal Education Commission/ KZ202210005009/R&D Program of Beijing Municipal Education Commission/ Switzerland 2023/08/26 Bioengineering (Basel). 2023 Jul 31; 10(8):909. doi: 10.3390/bioengineering10080909"