Martina Husáková

Exploration of Autoimmune Diseases Using Multi-agent Systems

Autoimmune disease is a group of pathological events identified by abnormal reactions of the immune system against self-structures of the organism. Pathogenesis of autoimmune diseases is multi-factorial. Genetics, infections, and environmental factors can support the progress of the autoimmunity. We investigated this mechanism using in vivo or in vitro approaches. Main aim of this manuscript is to explore the autoimmunity with in-silico approach - multi-agent systems. The preliminary research finds out which results can be acquired using factual data applied for building the multi-agent-based model. Preliminary computational model integrates one of the common aspects of autoimmune diseases - abnormal behaviour of B-cells during their organogenesis.

Variable Elimination Approaches for Data-Noise Reduction in 3D QSAR Calculations

In the last several decades, the drug research has moved to involve various IT technologies in order to rationalize the design of novel bioactive chemical compounds. An important role among these computer-aided drug design (CADD) methods is played by a technique known as quantitative structure-activity relationship (QSAR). The approach is utilized to find a statistically significant model correlating the biological activity with more or less extent data derived from the chemical structures. The present article deals with approaches for discriminating unimportant information in the data input within the three dimensional variant of QSAR – 3D QSAR. Special attention is turned to uninformative and iterative variable elimination (UVE/IVE) methods applicable in connection with partial least square regression (PLS). Herein, we briefly introduce 3D QSAR approach by analyzing 30 antituberculotics. The analysis is examined by four UVE/IVE-PLS based data-noise reduction methods.

Combating Infectious Diseases with Computational Immunology

Computational immunology aims at investigation of the immunity with computer science, mathematics, physics or statistics. Immune simulator is one of the outputs of research in computational immunology. Development of immune simulator is derived from the analysis of application domain and design of simulator with particular conceptual modelling language. Conceptualization is then used for simulator programming. The paper reviews and compares the most cited approaches of conceptual modelling and computational models building.

The Usage of the Agent Modeling Language for Modeling Complexity of the Immune System

Immune system is complex system which is composed of thousands entities interacting with each other. If we want to understand the immune system behavior, we can use the bottom-up approach investigating interactions occurring in the low-levels (e. g. molecular level) where particular biological entities exist. Multi-agent systems are bottom-up approach used for exploration of the immunity, but the complexity complicates clarifying immune processes. The paper investigates the Agent Modeling Language (AML) for conceptual modeling of particular immune properties and processes. T-cell dependent B-cell activation is used as the case study for finding out if the language can offer value added for conceptual modeling in computational immunology.

Ontology-Based Education Support System for Solving Emergency Incidents

Crisis situations influencing human lives require fast and faultless solutions. Decision making during emergency incidents is burdened with high uncertainty, complexity and risks. These situations can be effectively solved on the basis of complex knowledge that is received during education, training and experience. Non-professional rescuers often have a problem to decide which steps in which order have to be done in case of injured persons. Education of non-professionals is crucial and inevitable because of saving human lives. The main aim of the paper is to present the OWL ontology-based education support prototype using SWRL rules suggesting suitable solutions for particular emergency incident.

Immunity-Based Multi-Agent Coalition Formation for Elimination of Oil Spills

Occurrence of oil spills is a serious ecological problem which negatively influences the environment, especially water ecosystems. It is necessary to use efficient approaches that can reduce this danger as fast as possible. Multi-agent coalition formation is investigated in conjunction with the immunity-based algorithm CLONALG-Opt for elimination of oil spills.

SWRL-Based Recommendation System for Provision of the First Aid

The emergency management offers a collection of methods, strategies and frameworks how to efficiently solve particular emergency situation for saving human lives with minimum spending time. Fast and faultless decision making is inevitable in these situations. This requires huge amount of theoretical and practical experience which can be gained during studying of particular courses, study programmes, or where we are directly confronted with a reality. Non-professionals have a problem to decide about the best sequence of steps which has to be applied if a particular emergency event occurs. The main aim of this text is to present the SWRL-based prototype for decision making if the first aid is necessary.

Representation of Autoimmune Diseases with RDFS

Complex systems are systems consisting of many diverse and autonomous independent subsystems interacting with each other. Huge amount of interactions with many feedback loops complicate their investigation. Immune system is a typical complex system that attracts medical experts and also non-professionals especially because of its “ambient” nature and amazing complexity. Understandable information about immunity is required not only by the experts but also by the non-professionals. This paper is focused on development of the ontology providing fundamental facts about autoimmune diseases because these facts are not well-structured and presented for the end users on the web. The ontology should improve navigation among diverse pieces of information about these diseases and decrease information overloading.

Knowledge Representation Framework for Agent–Based Economic Systems in Smart City Context

The agent-based economic systems essentially need precise configuration data and access to knowledge from various information sources in order to function properly. Main sources of such information are national statistical economic data, regional statistics, company performance indicators, etc. Generally, information sources of various formats and levels of detail are to be used. The main aim of the paper is to present a general framework for knowledge management used in the smart city context, allowing efficient employment and distribution of such data. The knowledge layer serves as an ontological intermediary between information resources and agents themselves, and is used mainly for improvement of the model efficiency especially in the following areas: (1) inter-agent communication, (2) system parameters configuration, (3) meta-data for improved search processes, and (4) unification of data exchange.

Using Concept Maps in Education of Immunological Computation and Computational Immunology

The paper presents development of the concept map and the concept map-based web portal for education in immunological computation and computational immunology. The concept map should help with learning of students who do not study medicine, pharmacy or similar study programs and support active learning on the basis of collaborative development of the concept map by students with the assistance of a teacher.