2026-06-01T06:01:28Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/320612026-02-03T12:29:27Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Segura Ortiz, Adrián author García-Nieto, José Manuel author Aldana-Montes, José Francisco author 2024 This study introduces an innovative memetic algorithm for optimizing the consensus of well-adapted techniques for the inference of gene regulation networks. Building on the methodology of a previous proposal (GENECI), this research adds a local search phase that incorporates prior knowledge about gene interactions, thereby enhancing the optimization process under the influence of domain expert. The algorithm focuses on the evaluation of candidate solutions through a detailed evolutionary process, where known gene interactions guide the evolution of such solutions (individuals). This approach was subjected to rigorous testing using benchmarks from editions 3 and 4 of the DREAM challenges and the yeast network of IRMA, demonstrating a significant improvement in accuracy compared to previous related approaches. The results highlight the effectiveness of the algorithm, even when only 5% of the known interactions are used as a reference. This advancement represents a significant step in the inference of gene regulation networks, providing a more precise and adaptable tool for genomic research. https://hdl.handle.net/10630/32061 Optimización matemática Bioinformática Exploiting medical-expert knowledge via a novel memetic algorithm for the inference of gene regulatory networks.