Innate immune memory and its application to artificial immune systems.

The study of innate immune-based algorithms is an important research domain in Artificial Immune System (AIS), such as Dendritic Cell Algorithm (DCA), Toll-Like Receptor algorithm (TLRA). The parameters in these algorithms usually require either manually pre-defined usually provided by the immunologists, or empirically derived from the training dataset, and result in poor self-adaptation and self-learning. The fundamental reason is that the original innate immune mechanisms lack adaptive biological theory. To solve this problem, a theory called â€ËœTrained Immunity™ or Innate Immune Memory (IIM)™ that thinks innate immunity can also build immunological memory to enhance the immune system™s learning and adaptive reactions to the second stimulus is introduced into AIS to improve the innate immune algorithms™ adaptability. In this study, we present an overview of IIM with particular emphasis on analogies in the AIS world, and a modified DCA with an effective automated tuning mechanism based on IIM (IIM-DCA) to optimize migration threshold of DCA. The migration threshold of Dendritic Cells (DCs) determines the lifespan of the antigen collected by DCs, and directly affect the detection speed and accuracy of DCA. Experiments on real datasets show that our proposed IIM-DCA which integrates Innate Immune Memory mechanism delivers more accurate results.

A Novel Breast Cancer Diagnosis Scheme With Intelligent Feature and Parameter Selections.

BACKGROUND AND OBJECTIVE: Breast cancer is the most commonly occurring cancer among women, which contributes to the global death rate. The key to increasing the survival rate of affected patients is early diagnosis along with appropriate treatments. Manual methods for breast cancer diagnosis fail due to human errors, inaccurate diagnoses, and are time-consuming when demands are high. Intelligent systems based on Artificial Neural Network (ANN) for automated breast cancer diagnosis are powerful due to their strong decision-making capabilities in complicated cases. Artificial Bee Colony, Artificial Immune System, and Bacterial Foraging Optimization are swarm intelligence algorithms that solve combinatorial optimization problems. This paper proposes two novel hybrid Artificial Bee Colony (ABC) optimization algorithms that overcome the demerits of standard ABC algorithms. First, this paper proposes a hybrid ABC approach called HABC, in which the standard ABC optimization is hybridized with a modified clonal selection algorithm of the Artificial Immune System that eliminates the poor exploration capabilities of standard ABC optimization. Further, this paper proposes a novel hybrid Artificial Bee Colony (Hybrid ABC) optimization where the strong explorative capabilities of the chemotaxis phase of the bacterial foraging optimization are integrated with a spiral model-based exploitative phase of the ABC by which the proposed Hybrid ABC overcomes the demerits of poor exploration and exploitation of the standard ABC algorithm. METHODS: In this work, the two proposed hybrid approaches were used in concurrent feature selection and parameter optimization of an ANN model. The proposed algorithm is implemented using various back-propagation algorithms, including resilient back-propagation (HABC-RP and Hybrid ABC-RP), Levenberg Marquart (HABC-LM and Hybrid ABC-LM), and momentum-based gradient descent (HABC-MGD and Hybrid ABC-GD) for parameter tuning of ANN. The Wisconsin breast cancer dataset was used to evaluate the performance of the proposed algorithms in terms of accuracy, complexity, and computational time. RESULTS: The mean accuracy of the proposed HABC-RP was 99.14% and 99.54% for Hybrid ABC which is better than the results found in the existing literature. HABC-RP attained a sensitivity of 98.32%, a specificity of 99.63%, and a precision of 99.38% whereas Hybrid ABC attained sensitivity of 99.08% and Specificity of 99.81%. CONCLUSIONS: HABC-RP and Hybrid ABC-RP yielded high accuracy with a low complexity ANN structure compared to other variants. After evaluation, interestingly it is found that the Hybrid ABC-RP has achieved the highest mean accuracy of 99.54% with low complexity of 10.25 mean connections when compared to other variants proposed in this paper. It can be concluded that the concurrent selection of input features and tuning of parameters of ANN plays a vital role in increasing the accuracy of a breast cancer diagnosis. The proposed HABC-RP and Hybrid ABC-RP showed better results when compared to the existing breast cancer diagnosis systems taken for comparison. In the future, the proposed two-hybrid approaches can be used to generate optimal thresholds for the segmentation of tumors in abnormal images. HABC and Hybrid ABC can be used for tuning the parameters of various classifiers.

Multiresolution dendritic cell algorithm for network anomaly detection.

Anomaly detection in computer networks is a complex task that requires the distinction of normality and anomaly. Network attack detection in information systems is a constant challenge in computer security research, as information systems provide essential services for enterprises and individuals. The consequences of these attacks could be the access, disclosure, or modification of information, as well as denial of computer services and resources. Intrusion Detection Systems (IDS) are developed as solutions to detect anomalous behavior, such as denial of service, and backdoors. The proposed model was inspired by the behavior of dendritic cells and their interactions with the human immune system, known as Dendritic Cell Algorithm (DCA), and combines the use of Multiresolution Analysis (MRA) Maximal Overlap Discrete Wavelet Transform (MODWT), as well as the segmented deterministic DCA approach (S-dDCA). The proposed approach is a binary classifier that aims to analyze a time-frequency representation of time-series data obtained from high-level network features, in order to classify data as normal or anomalous. The MODWT was used to extract the approximations of two input signal categories at different levels of decomposition, and are used as processing elements for the multi resolution DCA. The model was evaluated using the NSL-KDD, UNSW-NB15, CIC-IDS2017 and CSE-CIC-IDS2018 datasets, containing contemporary network traffic and attacks. The proposed MRA S-dDCA model achieved an accuracy of 97.37%, 99.97%, 99.56%, and 99.75% for the tested datasets, respectively. Comparisons with the DCA and state-of-the-art approaches for network anomaly detection are presented. The proposed approach was able to surpass state-of-the-art approaches with UNSW-NB15 and CSECIC-IDS2018 datasets, whereas the results obtained with the NSL-KDD and CIC-IDS2017 datasets are competitive with machine learning approaches.

An artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers.

Endometrial cancer is one of the most common gynecological malignancies in developed countries. The prevention of the recurrence of endometrial cancer has always been a clinical challenge. Endometrial cancer is asymptomatic in the early stage, and there remains a lack of time-series correlation patterns of clinical pathway transfer, recurrence, and treatment. In this study, the artificial immune system (AIS) combined with bootstrap sampling was compared with other machine learning techniques, which included both supervised and unsupervised learning categories. The back propagation neural network, support vector machine (SVM) with a radial basis function kernel, fuzzy c-means, and ant k-means were compared with the proposed method to verify the sensitivity and specificity of the datasets, and the important factors of recurrent endometrial cancer were predicted. In the unsupervised learning algorithms, the AIS algorithm had the highest accuracy (83.35%), sensitivity (77.35%), and specificity (92.31%); in supervised learning algorithms, the SVM algorithm had the highest accuracy (97.51%), sensitivity (95.02%), and specificity (99.29%). The results of our study showed that histology and chemotherapy are important factors affecting the prediction of recurrence. Finally, behavior code and radiotherapy for recurrent endometrial cancer are important factors for future adjuvant treatment.

Implementing sequence-based antigenic distance calculation into immunological shape space model.

BACKGROUND: In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza "swine flu" pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational model that can capture the effect of pre-existing immunity on influenza vaccine responses. The model predicts the region of the virus hemagglutinin (HA) protein targeted by antibodies after vaccination as well as the level of cross-reactivity induced by the vaccine. We tested our model by simulating a scenario similar to the 2009 pandemic vaccine and compared the results to antibody binding data obtained from human subjects vaccinated with the monovalent 2009 H1N1 influenza vaccine. RESULTS: We found that both specificity and cross-reactivity of the antibodies induced by the 2009 H1N1 influenza HA protein were affected by the viral strain the individual was originally exposed. Specifically, the level of antigenic relatedness between the original exposure HA antigen and the 2009 HA protein affected antigenic-site immunodominance. Moreover, antibody cross-reactivity was increased when the individual's pre-existing immunity was specific to an HA protein antigenically distinct from the 2009 pandemic strain. Comparison of simulation data with antibody binding data from human serum samples demonstrated qualitative and quantitative similarities between the model and real-life immune responses to the 2009 vaccine. CONCLUSION: We provide a novel method to evaluate expected outcomes in antibody specificity and cross-reactivity after influenza vaccination in individuals with different influenza HA antigen exposure histories. The model produced similar outcomes as what has been previously reported in humans after receiving the 2009 influenza pandemic vaccine. Our results suggest that differences in cross-reactivity after influenza vaccination should be expected in individuals with different exposure histories.

On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation.

Understanding which function classes are easy and which are hard for a given algorithm is a fundamental question for the analysis and design of bio-inspired search heuristics. A natural starting point is to consider the easiest and hardest functions for an algorithm. For the (1+1) EA using standard bit mutation (SBM) it is well known that OneMax is an easiest function with unique optimum while Trap is a hardest. In this paper we extend the analysis of easiest function classes to the contiguous somatic hypermutation (CHM) operator used in artificial immune systems. We define a function MinBlocks and prove that it is an easiest function for the (1+1) EA using CHM, presenting both a runtime and a fixed budget analysis. Since MinBlocks is, up to a factor of 2, a hardest function for standard bit mutations, we consider the effects of combining both operators into a hybrid algorithm. We rigorously prove that by combining the advantages of k operators, several hybrid algorithmic schemes have optimal asymptotic performance on the easiest functions for each individual operator. In particular, the hybrid algorithms using CHM and SBM have optimal asymptotic performance on both OneMax and MinBlocks. We then investigate easiest functions for hybrid schemes and show that an easiest function for a hybrid algorithm is not just a trivial weighted combination of the respective easiest functions for each operator.

An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems.

Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we propose an approach to self-healing swarm robotic systems and take inspiration from the process of granuloma formation, a process of containment and repair found in the immune system. We use a case study of a swarm performing team work where previous works have demonstrated that partially failed robots have the most detrimental effect on overall swarm behaviour. We have developed an immune inspired approach that permits the recovery from certain failure modes during operation of the swarm, overcoming issues that effect swarm behaviour associated with partially failed robots.

Analysis of Randomised Search Heuristics for Dynamic Optimisation.

Dynamic optimisation is an area of application where randomised search heuristics like evolutionary algorithms and artificial immune systems are often successful. The theoretical foundation of this important topic suffers from a lack of a generally accepted analytical framework as well as a lack of widely accepted example problems. This article tackles both problems by discussing necessary conditions for useful and practically relevant theoretical analysis as well as introducing a concrete family of dynamic example problems that draws inspiration from a well-known static example problem and exhibits a bi-stable dynamic. After the stage has been set this way, the framework is made concrete by presenting the results of thorough theoretical and statistical analysis for mutation-based evolutionary algorithms and artificial immune systems.

QML-AiNet: An immune network approach to learning qualitative differential equation models.

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet to address the scalability and multimodal search space issues of qualitative model learning has been investigated. More importantly, to further improve the efficiency of QML-AiNet, we also modify the mutation operator according to the features of discrete qualitative model space. Experimental results show that the performance of QML-AiNet is comparable to QML-CLONALG, a QML system using the clonal selection algorithm (CLONALG). More importantly, QML-AiNet with the modified mutation operator can significantly improve the scalability of QML and is much more efficient than QML-CLONALG.

A lifelong learning hyper-heuristic method for bin packing.

We describe a novel hyper-heuristic system that continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; and representative problems and heuristics are incorporated into a self-sustaining network of interacting entities inspired by methods in artificial immune systems. The network is plastic in both its structure and content, leading to the following properties: it exploits existing knowledge captured in the network to rapidly produce solutions; it can adapt to new problems with widely differing characteristics; and it is capable of generalising over the problem space. The system is tested on a large corpus of 3,968 new instances of 1D bin-packing problems as well as on 1,370 existing problems from the literature; it shows excellent performance in terms of the quality of solutions obtained across the datasets and in adapting to dynamically changing sets of problem instances compared to previous approaches. As the network self-adapts to sustain a minimal repertoire of both problems and heuristics that form a representative map of the problem space, the system is further shown to be computationally efficient and therefore scalable.

El sistema inmunológico como herramienta diagnóstica de enfermedades reumatológicas / The immune system as a diagnosis tool of rheumatologic diseases

Introducción: los sistemas biológicos han sido objeto de muchas observaciones y recientemente se han convertido en modelos para ser emulados en diversos ambientes y ofrecer soluciones a problemas de la vida real. El sistema inmune es uno de los más representativos y en la actualidad constituye motivo de inspiración para la implementa-ción de sistemas computacionales que respondan a diversas tareas, constituyendo los Sistemas Inmunes Artificiales. Objetivo: este estudio busca desarrollar mecanismos computacionales inspirados en la inmunología para el diagnóstico de enfermedades reumatológicas que contribuyan en la educación y la toma de decisiones diagnósticas en reuma-tología. Se pretende obtener una herramienta computacional que, partiendo de un conjunto de historias clínicas como datos de entrenamiento, obtenga una efectividad en el diagnóstico comparable a los sistemas de clasificación de documentos actuales. El sistema está inspirado en la interacción entre los tejidos y los linfocitos B, y se apoya en conceptos de la teoría de la información para extraer relaciones entre términos. Los linfocitos B tendrán la función de discriminar la enfermedad reumatológica de un paciente con base en su historia clínica. Materiales y métodos: se utilizó un conjunto de datos compuesto por 54 historias clínicas de 54 pacientes en reumatología, entre los cuales 21 padecían artritis reumatoide, y el resto padecían otras enfermedades reumatológicas. El conjunto de datos se dividió en dos grupos: pacientes con artritis reumatoide y pacientes sin artritis reumatoide. Se hizo un procesamiento manual de las historias clínicas para eliminar toda la información que no fuera relevante para el sistema en la tarea de diagnóstico. La efectividad del sistema fue comparada frente a otros tres algoritmos de clasificación de texto ampliamente utilizados en tareas de clasificación de documentos (ID3, BayesNet y PsoSVM). Resultados: el sistema obtuvo resultados de efectividad prometedores en comparación con los demás algoritmos, con un promedio de 87,65% de efectividad en el diagnóstico. Sin embargo, debido a la limitación de datos, cabe la posibilidad de sesgo en los resultados. Se observó, como se había previsto, que los anticuerpos que representan la información en varios casos son redundantes. Adicionalmente, la información que representan no corresponde necesariamente a conocimiento médico, sino a reglas de clasificación de texto. Conclusiones: la teoría de la información, ayudada por la teoría del sistema inmunológico adapta-tivo y un mecanismo de señalización, muestra tener un potencial grande para la clasificación de historias clínicas. Debido a la posibilidad de sesgo observada en los resultados, será necesario realizar experimentos adicionales sobre un conjunto de historias clínicas más numeroso y más heterogéneo. Aunque entre los experimentos no se obtuvo anticuerpos que representaran claramente los conceptos, de tal manera que puedan ayudar a un profesional médico en el aprendizaje para la toma de decisiones, el trabajo a seguir consiste en adaptar técnicas de procesamiento de lenguaje natural (i.e., sintaxis y semántica), para así llegar a un sistema de obtención de conocimiento en lugar de un sistema de obtención de reglas de clasificación de texto.

Introduction: the biological systems have been observed and analyzed carefully and they have transformed into models to be emulated in many types of scenery and these offer solutions to problems of the real life, more recently. The immune system is one of the most representatives and at the moment is used for implementation of computational systems to respond to many tasks, constituting the Artificial Immune Systems. Objective: in this work a computational method inspired by immunology for diagnosis of rheuma-tologic diseases is developed. The goal is to obtain a computational tool that, given a group of clinical histories as training data, performs rheumatologic diagnosis comparable to the current systems used in document classification. The proposed tool is expected to contribute in education and decision making in rheumatologic diagnosis. The proposed system is inspired by the interaction between tissues and B lymphocytes, and it relies on concepts of information theory to extract relationships among terms. The B lymphocytes will have the function of discriminating a patient’s rheumatic diseases based on its clinical history. Materials and methods: a dataset consisting of 54 medical records from 54 patients with rheumatologic diseases was used; 21 patients suffered rheumatoid arthritis, and the rest suffered other rheumatologic diseases. The dataset was divided into two groups: patients with and without rheumatoid arthritis. A manual process on the clinical histories was performed to eliminate the irrelevant information in the diagnosis task. The effectiveness of the system was compared to other three text classification algorithms widely used in document classification tasks, namely, ID3, BayesNet and PsoSVM. Results: the proposed system obtained promising results in comparison with other algorithms, with an average of 87,65% effectiveness in the diagnosis. However, due to the limitation of the data, there is a possibility that the results are biased. It was observed, as expected that the antibodies that represent the information in several cases are redundant. Additionally, the information that it represents not necessarily corresponds to medical knowledge, but to rules of text classification. Conclusions: information theory in conjunction with an adaptive immune system and a signaling mechanism showed great potential for the classi-fication of medical records. Due to the possibility of a bias in the results, it will be necessary to carry out additional experiments on a larger and more heterogeneous group of medical records. From the experiments, antibodies that clearly represented concepts explaining rheumatoid arthritis were not obtained, which could help medical trainees in the learning process and medical doctors in decision making. Therefore, in future work, the task to continue consists on adapting natural language processing methods (i.e., syntax and semantics) to obtain a knowledge extraction system instead of a set of rules for text classification.

Recent advances in gene expression data clustering: a case study with comparative results

Several advanced techniques have been proposed for data clustering and many of them have been applied to gene expression data, with partial success. The high dimensionality and the multitude of admissible perspectives for data analysis of gene expression require additional computational resources, such as hierarchical structures and dynamic allocation of resources. We present an immune-inspired hierarchical clustering device, called hierarchical artificial immune network (HaiNet), especially devoted to the analysis of gene expression data. This technique was applied to a newly generated data set, involving maize plants exposed to different aluminum concentrations. The performance of the algorithm was compared with that of a self-organizing map, which is commonly adopted to deal with gene expression data sets. More consistent and informative results were obtained with HaiNet.