RT Conference Proceedings
T1 Exploiting medical-expert knowledge via a novel memetic algorithm for the inference  of gene regulatory networks.
A1 Segura Ortiz, Adrián
A1 García-Nieto, José Manuel
A1 Aldana-Montes, José Francisco
K1 Optimización matemática
K1 Bioinformática
AB 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.
YR 2024
FD 2024
LK https://hdl.handle.net/10630/32061
UL https://hdl.handle.net/10630/32061
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026