Innate immune memory and its application to artificial immune systems

Wang, Dongmei; Liang, Yiwen; Dong, Hongbin; Tan, Chengyu; Xiao, Zhenhua; Sai, Liu

doi:10.1007/s11227-021-04295-1

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…As the global pandemic of the new coronavirus (COVID-19) has shaken the world since 2020, the study of the human immune system has piqued the interest of many researchers. The Artificial Immune Systems (AIS) is the simulation algorithm of human body defense systems and is applied to solve many research fields, such as clustering/classification, fault detection, combinatorial optimization, and learning problems [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Routing Optimization with Cross-Docking Based on an Artificial Immune System in Logistics Management

Chuang

2023

Mathematics

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Background: Manufacturing companies optimize logistics network routing to reduce transportation costs and operational costs in order to make profits in an extremely competitive environment. Therefore, the efficiency of logistics management in the supply chain and the quick response to customers’ demands are treated as an additional source of profit. One of the warehouse operations for intelligent logistics network design, called cross-docking (CD) operations, is used to reduce inventory levels and improve responsiveness to meet customers’ requirements. Accordingly, the optimization of a vehicle dispatch schedule is imperative in order to produce a routing plan with the minimum transport cost while meeting demand allocation. Methods: This paper developed a two-phase algorithm, called sAIS, to solve the vehicle routing problem (VRP) with the CD facilities and systems in the logistics operations. The sAIS algorithm is based on a clustering-first and routing-later approach. The sweep method is used to cluster trucks as the initial solution for the second phase: optimizing routing by the Artificial Immune System. Results: In order to examine the performance of the proposed sAIS approach, we compared the proposed model with the Genetic Algorithm (GA) on the VRP with pickup and delivery benchmark problems, showing average improvements of 7.26%. Conclusions: In this study, we proposed a novel sAIS algorithm for solving VRP with CD problems by simulating human body immune reactions. The experimental results showed that the proposed sAIS algorithm is robustly competitive with the GA on the criterion of average solution quality as measured by the two-sample t-test.

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Section: Introductionmentioning

confidence: 99%

Vehicle Routing Optimization with Cross-Docking Based on an Artificial Immune System in Logistics Management

Chuang

2023

Mathematics

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Improved clonal selection algorithm based on the directional update strategy

et al. 2023

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At present, many cloning selection algorithms have been studied, and improvements have been made to the cloning, mutation and selection steps. However, there is a lack of research on the optimization of the updating operation steps. The clonal selection algorithm is traditionally updated through a random complement of antibodies, which is a blind and uncertain process. The added antibodies may gather near a local optimal solution, resulting in the need for more iterations to obtain the global optimal solution. To solve this problem, our improved algorithm introduces a crowding degree factor in the antibody updating stage to determine whether there is crowding between antibodies. By eliminating antibodies with high crowding potential and poor affinity, the improved algorithm guides the antibodies to update in the direction of the global optimal solution and ensures stable convergence with fewer iterations. Experimental results show that the overall performance of the improved algorithm is 1% higher than that of the clonal selection algorithm and 2.2% higher than that of the genetic algorithm, indicating that the improved algorithm is effective. The improved algorithm is also transplanted to other improved clonal selection algorithms, and the overall performance is improved by 0.97%, indicating that the improved algorithm can be a beneficial supplement to other improved clonal selection algorithms.

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Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool

Beltrán,

Belén,

Yáñez

et al. 2024

BMC Bioinformatics

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Viral proteins that evade the host’s innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and time-consuming due to the complexity of virus-host interactions. Leveraging advancements in computational biology, we present VirusHound-II, a novel tool that utilizes machine learning techniques to predict viral proteins evading the innate immune response with high accuracy. We evaluated a comprehensive range of machine learning models, including ensemble methods, neural networks, and support vector machines. Using a dataset of 1337 viral proteins known to evade the innate immune response (VPEINRs) and an equal number of non-VPEINRs, we employed pseudo amino acid composition as the molecular descriptor. Our methodology involved a tenfold cross-validation strategy on 80% of the data for training, followed by testing on an independent dataset comprising the remaining 20%. The random forest model demonstrated superior performance metrics, achieving 0.9290 accuracy, 0.9283 F1 score, 0.9354 precision, and 0.9213 sensitivity in the independent testing phase. These results establish VirusHound-II as an advancement in computational virology, accessible via a user-friendly web application. We anticipate that VirusHound-II will be a crucial resource for researchers, enabling the rapid and reliable prediction of viral proteins evading the innate immune response. This tool has the potential to accelerate the identification of therapeutic targets and enhance our understanding of viral evasion mechanisms, contributing to the development of more effective antiviral strategies and advancing our knowledge of virus-host interactions.

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Innate immune memory and its application to artificial immune systems

Cited by 3 publications

References 39 publications

Vehicle Routing Optimization with Cross-Docking Based on an Artificial Immune System in Logistics Management

Vehicle Routing Optimization with Cross-Docking Based on an Artificial Immune System in Logistics Management

Improved clonal selection algorithm based on the directional update strategy

Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool

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