Title: Metaheuristic gene regulatory networks inference using discrete crow search algorithm and quantitative association rules

Authors: Makhlouf Ledmi; Mohammed El Habib Souidi; Aboubekeur Hamdi-Cherif; Abdeldjalil Ledmi; Hichem Haouassi; Chafia Kara-Mohamed

Addresses: Department of Computer Sciences – ICOSI Lab, Abbes Laghrour University of Khenchela, Khenchela 40000, Algeria ' Department of Computer Sciences – ICOSI Lab, Abbes Laghrour University of Khenchela, Khenchela 40000, Algeria ' Department of Computer Science, Ferhat Abbas University of Setif 1, Setif, 19000, Algeria ' Department of Computer Sciences – ICOSI Lab, Abbes Laghrour University of Khenchela, Khenchela 40000, Algeria ' Department of Computer Sciences – ICOSI Lab, Abbes Laghrour University of Khenchela, Khenchela 40000, Algeria ' Department of Computer Science – LRSD Lab, Ferhat Abbas University of Setif 1, Setif 19000, Algeria

Abstract: Gene regulatory networks (GRNs) inference appeared as valuable tools for detecting irregularities in cell regulation. Association rule mining (ARM) encompasses specific data mining methods capable of inferring unknown associations between genes. In response to the scarcity of ARM-based GRN inference, a novel metaheuristic algorithm, DCSA-QAR, is presented. This algorithm infers quantitative association rules by discretising the crow search algorithm. A first series of experiments involved comparison with five metaheuristic algorithms on six datasets. The results showed that, for Co-citation and YeastNet datasets, our algorithm was first in precision (100%), specificity (100%) and score (3.75). A second series of experiments involved nine information-theoretic algorithms through the DREAM3 and SOS networks. The average results on DREAM3 datasets are compensated by the SOS real datasets results: the best in accuracy, and true positives. As an overall appraisal, DCSA-QAR can be considered as a good candidate for ARM-based metaheuristic GRNs inference.

Keywords: artificial intelligence; bioinformatics; gene regulatory networks; GRNs; data mining; soft computing; mining association rules.

DOI: 10.1504/IJDMB.2025.147033

International Journal of Data Mining and Bioinformatics, 2025 Vol.29 No.3, pp.278 - 312

Received: 26 Aug 2023
Accepted: 02 Jan 2024
Published online: 10 Jul 2025 *

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