Biological databases using object-oriented system analysis Текст научной статьи по специальности «Фундаментальная медицина»

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UDC 004:591.5:612:616-006 https://doi.org/10.15407/biotech12.03.005

BIOLOGICAL DATABASES: USING OBJECT-ORIENTED SYSTEM ANALYSIS

O. M. KLYUCHKO1, L. P. BUCHATSKY2, O. V. MELEZHYK3

1Kavetsky Institute of Experimental Pathology, Oncology and Radiobiology of the National

Academy of Sciences of Ukraine, Kyiv 2Taras Shevchenko National University of Kyiv, Ukraine 3Open International University of Human Development "Ukraine", Kyiv, Ukraine

E-mail: kelenaXX@ukr.net

Received 26.02.2019 Revised 17.04.2019 Accepted 05.07.2019

The aim of the work was to develop electronic database of biological organisms (fishes, viruses) on the base of the methods that have been used before in mathematics, engineering, physics. The methods of object-oriented system analysis, construction of models of subject spaces (problem space, solution space), E#-diagrams, of constructing databases and others were applied. The examples of prototypes of modern databases with information about fishes developed over the past years in Western European and American countries were presented. An overview of applied methods, concepts and terminology from the areas of object-oriented system analysis, database designing was disclosed. In conclusions the description of constructed database and practical recommendations for the development of databases with the information about domestic biological organisms for electronic information systems are given.

Key words: object-oriented system analysis, biological objects, electronic information systems, fishes, viruses.

Electronic information systems with databases for biotechnology, environmental protection of bioobjects and other biological sciences. Electronic information technology (IT) penetrated deeply into our life — industry, technology, science; they have become a feature that characterizes our reality and level of development of the countries. Being an integrative part of the Europe and modern industrial world, Ukraine is also incorporated into the world scientific and technological progress and has all opportunities to develop progressively new technologies in its own territories. Over the last 30 years, computer technique and related new technologies — network, Internet technologies, and others — have become widespread even in the everyday life of the people. Consequently, the information and computer revolution in classical biology and medicine has happened

too. To the solution of these problems the people came about 30 years ago. One of the most actual contemporary tasks is the creation of information systems (ISs) in medicine and biology, and the majority of these ISs operate with the data that have been ordered in electronic databases (DBs). In the last 20-25 years in medicine and in the last 15-20 years in biology, the number of new electronic ISs has been increased rapidly; they had their own peculiarities and specifics in comparison with similar systems in engineering, physics and other similar industries. The number of such ISs in biology and medicine is growing from day to day. Nowadays, at the beginning of the XXI century, the scientists in the field of information and computer technologies (ICTs) discussing further development of new software and computer technology specifically

for medicine and biology. In contemporary world there is an important idea to use such novel ICTs with DB for ordering of information about living organisms — for example fishes [1]; their accounting, descriptions and even for determination of such organisms on the base of these new technologies [2-20]. Sure, in base of electronic information systems (ISs) elaboration the real biological data have to be placed [1, 21-36]. Such primary versions of ISs in Ukraine have been done; technologies of their elaboration partially are described [37-49], and the data about different objects of fauna [21-36, 45, 46] and flora [50-52] may be ordered in them. For the success of such works it is necessary to use positive experience of prototypes development; either of practical or theoretical ways of these tasks solutions [2-20, 53-172]. Some of such prototypes were developed for monitoring of biodiversity changes in fauna or ecological changes also under anthropogenic pressure using ICTs [2-7, 9, 14-18, 46-48, 56, 59, 81, 148, 149, 153, 165, 169-172, 174-176, 178, 183, 186-188]. Some works of this article authors were defended by patents [32, 173-189]. Contemporary knowledge of ICTs technologies, electronic DBs construction were used for present work fulfillment [190-192].

A number of important reasons determine the necessity of the work on the creation of IS with the databases (DBs) in biology. Actually, the previous author's works [45, 46] are the continuation of the works done in present article, because relative databases developments [45] have to be based on the resuts of object-oriented system analysis from present publication. Primarly, the ICTs with databases were developed, first of all, for the needs of technology and such fields as mathematics, physics, statistics and economy (first of all, for energetic). These technologies began to be developed in biological sciences later because of the set of reasons: objects with which the biology deals with are much more complicated, main attention in contemporary societies is paid on high industry development, and so on. In general, ICT with databases comes in two ways to medicine and biology: through the acceptance of ISs that were developed earlier for other industries with further their transformation depending on the specifics of medicine and biology, and by creating of original types of these systems. Today, the samples of ISs with databases in biology are not numerical in comparison with technique, they do not satisfy all practical needs, and therefore almost all new works in these industries

are valuable and find their application. In addition, the material in these items that was given our publications has the practical value because it can be the basis for the creation of new types of systems.

Because the specifics of biology ever were the works with complex objects, living systems, multifactorial influences on them, and etc. thus, an analysis of such specificity and its discussion is really relevant. It is also important that because of peculiarity and specifics of medical and biological objects, accumulation in these areas of great numbers of the results of experiments, observations, etc. — the use of modern information and computer technologies with databases will contribute to the next progress in these areas, since it will allow ones to analyze and to process a huge (and constantly growing!) amount of the data. With the use of previous technologies this was impossible indeed, and this led to the loss with time a significant part of obtained results (including the results of expensive high-precision experiments).

Importance of object-oriented system analysis in process of electronic databases construction. The construction and development of electronic databases (DB) of biological organisms (local DB (separated) or as a part of ISs) is an important task still. Such databases should, as far as possible, contain a collection of information about individual biological organisms, their communities and other related information. The scope of such databases usage is broad: solution of problems in biotechnology, molecular biology and genetics; in preserving of species diversity, protecting the environment from harmful influences; database as the electronic determinants of living organisms and many other applications [37-49]. In our previous works we have provided already the information on developing a database of biological organisms, for example, Noctuidae (Lepidoptera) and some other Arthropodae [37, 45, 46]. However, the methods of DB constructing which have been demonstrated in our publications [37-49, 173-176, 178, 183, 186-188], are standard for use when creating a database of all biological organisms, not just Arthropodae [2-20]. Therefore, in this publication we woud like to demonstrate the possibility of their use for other groups of living organisms: fishes and viruses — pathogens of fishes' diseases, in order to involve our fauna representatives as fully as possible in our works. In addition, due to the limited volume of our previous publications,

we were not able to pay necessary attention to the important set of works preceding the process of database development — to object-oriented system analysis of virtual space containing the objects that have to be included into the developed electronic database.

Object-oriented system analysis of the investigated space is extremely important for the reasons of the most rational design of the databases. Our Nature includes a huge number of different groups of organisms. Each of these organisms has its own characteristics, which must be included into the database— there are billions of records. In case of insufficiently qualified determining of such characteristics, establishment of wrong links between organisms, etc., the DBs become irrationally constructed, overloaded with unnecessary details and lack of important information. This leads to slowdown the information search, and even to impossibility of data mining in an imperfect database. Accordingly, the time of data mining in such imperfect databases increases. The situation is even more complicated in case of different databases combining, for example, if they were constructed at different universities. Consequently in present publication, basing on the authors' experience, some practical recommendations were given for the development of modern, perfect biological, Internet-based databases (primarily relational) taking into account the specifics of medical and biological objects, which confirms the relevance of the work performed.

Some examples of modern databases and information systems with information about the fishes. In this section, let's observe some examples of modern databases with information about fishes.

A. Global Information System On Fishes. A global biodiversity information system on fishes is called "FishBase" [16]. Its initial goal is to provide key facts on population dynamics for 200 major commercial fish species. Now DB of this IS grown to having a wide range of information on all fish species currently known in the world: taxonomy, biology, trophic ecology, life history, and uses, as well as historical data reaching back to 250 years.

At present, FishBase covers more than 33,000 fish species compiled from more than 52,000 references in partnership with more than 2,000 collaborators (>300,000 common names and >55,000 pictures). The breadth and depth of information in the database, combined with the analytical and graphical tools available in the web can satisfact different

needs of diverse groups of users (scientists, researchers, policy makers, fisheries managers, donors, conservationists, teachers and students). Its various applications are aimed for sustainable fisheries management, biodiversity conservation and environmental protection. FishBase is result of the work of non-profit, non-governmental organization engaged in the development and management of global databases on aquatic organisms, including their distribution and ecology. FishBase provides the database and web interface for free for 25 years.

B. A database of fish biotransformation rates for organic chemicals. A method published by other authors [17] for estimating whole-body in vivo metabolic biotransformation rate constants (kM) is applied to a database of measured laboratory bioconcentration factors and total elimination rate constants for fish. The method uses a kinetic mass balance model to estimate rates of chemical uptake and elimination when measured values previously were not reported. More than 5 400 measurements for more than 1 000 organic chemicals were critically reviewed to compile a database of 1 535 kM estimates for 702 organic chemicals. Biotransformation rates range over six orders of magnitude across a diverse domain of chemical classes and structures. Screening-level uncertainty analyses provide guidance for the selection and interpretation of kM values. In general, variation in kM estimates from different routes of exposure (water vs diet) and between fish species is approximately equal to the calculation uncertainty in kM values. Examples are presented of structure-biotransformation relationships. Biotransformation rate estimates in the database are compared with estimates of biodegradation rates from existing quantitative structure-activity relationship models. Modest correlations were found, suggesting some consistency in biotransformation capabilities between fish and microorganisms. Additional analyses to further explore possible quantitative structure-biotransformation relationships for estimating kM from chemical structure were encouraged, and recommendations for improving the database were provided.

C. A global database on freshwater fish species occurrence in drainage basins. In their actual publication the authors stated that nowadays the growing interest is devoted to global-scale approaches in ecology and evolution that examine patterns and determinants of species diversity and the

threats resulting from global change [18]. These analyses obviously require global datasets of species distribution. Freshwater systems house a disproportionately high fraction of the global fish diversity considering the small proportion of the earth's surface that they occupy, and are one of the most threatened habitats on Earth. The authors provided complete species lists for 3119 drainage basins covering more than 80% of the Earth surface using 14 953 fish species inhabiting permanently or occasionally freshwater systems. The database results from an extensive survey of native and non-native freshwater fish species distribution based on 1 436 published papers, books, grey literature and web-based sources. Alone or in combination with further datasets on species biological and ecological characteristics and their evolutionary history, this database represents a highly valuable source of information for further studies on freshwater macro ecology, macroevolution, biogeography and conservation.

D. Databases of freshwater fish species distributions. Another report about the databases of freshwater fish species distributions was suggested in [18]. The authors discussed the paradox of fishes species that approximately 40% of all described fish species inhabiting freshwaters, while the remaining 60% are marine habitants. Describing global scale freshwater fish diversity patterns, understanding the environmental drivers and evolutionary processes shaping such diversity and revealing the major human-related threats were the major goals that motivated the compilation of presented database. They conducted an extensive survey of freshwater fish species distribution based on 1 436 published papers, books, grey literature, databases and web-based sources, resulting in species lists for 3 119 drainage basins covering more than 80% of the Earth surface. To date, these databases have been used in several studies that have increased our understanding of freshwater fish species distributions. These studies allowed to accurately map global patterns of native, endemic and non-native freshwater fish species richness and to reveal their environmental and human-related determinants. The databases were also used to evaluate non-native species influence on native communities' structure, to forecast climate change effects on species extinction processes and to analyse effects of current and future scenarios of species introductions

on fish faunas homogenization processes. Recent studies also focused on analyzing the influence of past river connections on the present distribution of native fish species, on analyzing geographical and other differences in diversification rates and origin of actinopterygian fish families, and on evaluating human-related extinction drivers.

Methods of object analysis, constructing of models of subject spaces when creating a biological database about fishes and viruses that they infect. This section deals with the consideration and the characteristics of the methods of analysis of subject space and model construction [191-192]. To solve such problems, the object analysis methods [190] have been developed, which is described below.

The main purpose of objective analysis is to represent a subject space (SbS) as a set of objects with their properties and characteristics that are sufficient for their defining and identification, as well as for determining the behavior of objects within the framework of the chosen system of concepts and abstractions.

For example, in present work we would like to make the database about fishes and viruses that they infect; respectively, for our object analysis in framework of this task solution we have to select primary such main objects as "Fish" and "Virus". Sure, with time in process of task solution, other objects may be added to these first two.

All SbS concepts are essences. Each object is a unique element; it has at least one property or characteristic and a unique identifier in a set of objects.

Analysis of SbS is carried out using object-oriented methods and corresponding standards. The ultimate objective of the object-oriented analysis of SbS is the definition of the object model (OM) with the help of selected objects, the relations between them and their properties and characteristics.

A. Brief overview of object-oriented methods for analyzing and constructing of models. For today, more than 50 object-oriented methods of SbS analysis are known, which have been practically tested. In present work we used only 3 of them:

1. Object-Oriented System Analysis (OOAS). This is a method, which allows identifying the entities and objects of SbS, defining their properties and relationships, as well as building on their basis an information model, a model of objects state and processes of presentation of data flows (dataflow) [190].

2. Object Oriented Analysis (OOA). This is a method, which provides simulation of OM and formation of requirements for SbS by means of a notion "entity-relationship ER", specification of data streams and related processes [190-192]. The scheme — ER-diagram for abstract objects "Fish" and "Virus" is given on Fig. 1.

3. Object Oriented Analysis and Design (OOAD). This is methodology, which is based on ER-modeling of entities and relationships in SbS object model. OOAD provides a system definition and organization of the data using structured diagrams, diagrams "entity-reations" and the matrix of information management [190].

The study of object-oriented methods varieties shows that they have many common features (for example, ER-modeling, Dataflow), as well as their specific features. Each developer of the method of object-oriented analysis invented his own new necessary concepts, which semantically coincide often with similar concepts in other methods. Therefore, the terminology the very conceptual apparatus used in this sphere does not always coincide in different methods.

B. Basic concepts of the methods of object analysis of SbS. To the basic concepts of the methods of SbS object analysis would belong the following:

SbS object is an abstract image with behavior that depends on its characteristics and relationships with other SbS objects.

Entity is a semantically important object or type of object, that exists in SbS in reality or it is an abstract concept, whose information must be known and / or recorded.

Concept is the value of some abstract entity of SbS, it is denoted by a unique name or identifier. A group of such concepts is a parent concept, which is obviously determined by some set of common attributes. The concept, along with its attributes is presented graphically in OM or in text form.

Attribute is an abstraction that has all abstracted essence of concepts. Each attribute is denoted by a name unique within the concept description. The set of grouped attributes denotes the identifier of this group. A group of attributes can be combined into a class and to have a class identifier.

Relationship is an abstraction of a set of relations that take place between different kinds of SbS objects which are abstracted as concepts. Each relationship has its unique identifier. Relationships can be textual or graphical. To formalize relationships between concepts, the auxiliary attributes and links to identifiers of these relationships are added. Some relationships are formed as consequence of other relationships existence.

Class is a set of objects with the same properties, operations, relationships, and semantics. Any object belongs to a class. A class is represented by various methods (for example, by lists of objects, operations,

— an object (a noun);

— a relation between objects (a verb);

— an attribute.

Fig. 1. ER-diagram for the database "Fishes And Their Virus Diseases": analysis for two main objects "Fish" and "Virus": a — fragment of ER-diagram (explanations see in text); b — symboic denotation of objects, relations and attributes at the scheme

b

states). Class is measured by the number of the samples, operations, etc.

Subject space (SbS) is what is analyzed in order to isolate a specific set of concepts (entities, objects) and relations between them. On the set of these concepts, the tasks are defined for the purpose of their automated solution. The subject space can be divided into the problems' space and the solutions' space. The problems' space includes the entities, the concepts of SbS, and the solutions' space is the set of software implementations of the tasks, including functional components that provide the solution of the tasks and functions of SbS represented in this space.

Allocation of SbS entities is carried out taking into account the differences determined by the relevant conceptual structures. SbS model is a set of precise definitions of concepts, objects and their characteristics, as well as a set of synonyms and classified logical relationships between these concepts.

Relations or links are established for model objects. There are static (permanent) relations that do not change or change rarely, and dynamic relations that have certain states and change during the operation of the system. The state of connections between objects can evolve over time.

The links between the objects can be following (Fig. 2):

• one-to-one relationship (1: 1) exists when one sample of an object of a certain class is associated with a single sample of another class, i.e. single instances of the classes are involved in communications;

• one-to-many relationship (1: N) exists when one samples of an object of a certain class is associated simultaneously with one or more samples of another class or of the same class;

• many to many relationship (M: N) exists when several samples of objects of two classes take part in relationships, i.e. one or more samples of another class is associated with one or more samples of the first class.

Hierarchical model of biological objects' organization and classification from the point of view of DB construction.

Hierarchical model is extremely important model of objects' organization either in living Nature or in object-oriented methods of programming; in our models we have to refect obviously this hierarchical type of objects' organization. In our previous publications [37] we have written already that hierarchical models in informatics are enough similar to hierarchy of living organisms in nature. As example of DB construction let's study now the pair of organisms: the fish (carp) and virus that cause its disease. It is known that for hierarchical classification of organisms in living nature, Carl Linnaeus proposed his scheme on 1 761 [70, 37]. Let's represent according to his scheme such well-known fish as ordinary carp (Cyprinus carpio L.) that belongs to animals from Eukaryota Domain (Fig. 3).

Further in our investigation we would like to unite in linked databases the information about fishes (for example, the carp Cyprinus carpio L.) and viruses that are the reasons of fishes' diseases. For example, for C. carpio it is known that they have such virus diseases: (spring viremia of carp, infectious necrosis of hematopoietic tissue, infectious necrosis of pancreas, viral necrosis of erythrocytes, herpesvirus infections, iridovirus infections, and etc.), which are characterized by acute flow with high mortality of fishes that causes significant economic losses to world aquaculture.

At the next step we have to describe at one scheme these 2 objects (carp-virus) with all their important characteristics and relations between objects. Such scheme one could see on Fig. 4, that is "EE-diagram" modified for the fish "Carp" (compare with Fig. 1)

Functional role of ER-diagram is to represent in whole all objects from the database with all their relations and with all characteristics of the objects that are called "attributes". Sure, characteristics those are important from the point of view of the database representation! For example, such characteristic as "Maria likes to fry carps"

Fig. 2. Power of reactions between the objects during the database elaboration:

"M: N" — "many-to-many". In this example, this means: "M species of fishes have been infected by N strains of viruses"

Domain Eukaryota

Kingdom Metazoa

Type Chordata

Subtype Craniata

Class Actinopterygii

Infraclass Teleostei

Series Cypriniformes

Family Cyprinidae

Genus Cyprinus

Specie Cyprinus carpio L.

Fig. 3. Scheme of ordinary carp (Cyprinus carpio L.) hierarchic classification

probably, is not so important for academic databases. But for such DB are too important the data (attributes) as gene sequences, specie, family, genus and other similar. For the representation of these 3 main notions of ER-diagram the special symbols are used (Figs. 1, 4): rectangle (object), rhombus (relation), oval (attribute). A rectangle (object) usually includes a noun — for example, the "fish". A rhombus (relation) is ever a verb — for example, "to infect". And an oval (attribute) ever includes characteristics: species (or other classification unit), color, dimensions, etc. Such diagram helps to analyze better all entities that we would like to include into the database and to represent them in their entirety. Without such analysis the construction of DB of a high quality would not be possible.

Since each species of fish may be infected by several viruses (N), and each virus can infect different species of fish (M), then the links between the objects have to be marked as "M:N", which actually means: "M species of fishes can be infected by N strains of viruses" (Fig. 2). As noted above, other variants are possible, like "M:1" — means "M species of fishes can be infected by the virus of one strain". Also "1:1" — means "one specie of fish can be infected by the single virus strain". Such abstraction allows us to design a high-quality database and to verify its accuracy in the visual diagram — "ER-diagram" in future.

Development of the database with information about fishes' on the base of "ER-diagram". The scheme "ER-diagram" we need for further development of the database with information about fishes on its basis. Let's study the representation of such database in the form of a set of tables, which are given on Fig. 5. It is easy to see that:

1. Objects of "ER-diagram" were turned into separate tables.

2. Attributes were turned into the fields of these tables (in our case — in the columns).

3. The relations between the objects were turned into relations between separate tables.

At the same time, the power of reations reflects the number of tables that need to be linked with the object; and the developer deside what number have to be. If the power of reation is 1, then the link is formed with one table, if "M" — with "M" numbers of tables.

Objects in our model can be characterized by the number of attributes. The lists of some attributes is given below. On Fig. 5 one can see the tables with the less number of attributes because of limited space of the page.

Fig. 4. Fragment of ER-diagram "Fishes And Their Virus Diseases" on example

of Cyprinus carpio L.

Object "Fish"

Attributes: Primary key

The name of the fish (in Latin, Ukrainian and Russian). Class Subclass Series Family Genus Species

Area of inhabitance. Biotopes of inhabitance. Objects of nutrition. Data of fishing. References to literary sources.

Comments. Other.

Object "Virus"

Attributes: Primary key

The name of the virus (in Latin, Ukrainian and Russian). Dimentions

Code of gene sequence (if it known) Name of disease Symptoms of disease References to literary sources.

Comments.

Other.

Relations between the tables using "keys." In order not to lose the information that is filled in the database of biological objects, the technique of "keys" is used.

"Keys" are necessary "tools" for relations establishing between the objects; they guarantee that the information from the database is accessable and it always may be obtained from there. Indeed, there is no sense to write thousands of data records into a structure if these data can not be used, if they are not accessible! Consequently, the "keys" are the elements of each table; they are related to each other through programmed links. By obtaining the information from one table, it is possible to obtaine the information from another table associated with it through a link that unites their keys. Usually, as a "key" serves a certain code (numbers or other symbols), and the links between such codes from individual tables in many modern software environments, even a young developer can make by themselves (Fig. 5).

At our tables (Fig. 5) there are 2 codes (or keys) have to be linked:

1) 1* (Cyprinus carpio L.) and

2) kB (Rhabdovirus carpio — Spring Viraemia of Carp Virus (SVCV)).

This means that when user in Internet will "click" on "Cyprinus carpio L." he will see consequently the information about (Rhabdovirus carpio (Spring Viraemia of Carp Virus (SVCV)) — virus that infects carp.

In addition, it is easy to follow in the diagram whether all entities are linked together. In this case it makes possible to access all records, so, the information from the database becomes fully available for everybody (the condition of the "integrity" of the data is fulfilled). Alternatively, if this condition is violated, the user will not be able to obtain the information from those parts of the database that are not connected by such links. However, using the methods described above, the programmer can easily recover connections based on the ER-diagram and linking the objects using keys. In the application of the abovedescribed methods, there are no differences between biological and technical databases and anybody for such purpose can study this technique using appropriate manual. The previous works of Klyuchko O.M. [45, 46] are the continuation of the works done in present article, because relative databases developments [45] have to be based on the resuts of object-oriented system analysis from present article.

Thus, according to the purpose of the work there were demonstrated that the methods for relation electronic databases construction, that were developed earlier for physics, engineering, etc. can be applied in other branches which traditionally do not refer to technical disciplines. Usually these were done by their acceptance in the new spheres of application, for example, in biology and medicine. In present publication, the following set of methods was used to develop databases with biological content: object-oriented system analysis (OOAS), object-oriented analysis (OOA) and OOAD (object-oriented analysis and design) — the latter methodology is based on ER-modeling of entities and relationships in the object model (ER-diagram).

Actually, the previous author's works [45, 46] are the continuation of the works done in present article, because relative databases developments [45] have to be based on the resuts of object-oriented system analysis from present publication. At the beginning of present article, a number of examples of electronic databases of biological objects that were developed in different countries of the

Primary key Ñame of species (Latin) Name of species (national) Photo Class Subclass Series Family Genus

1* Cyprinus carpió L. Короп звичайний Foto 1: http://www2.dnr. cornell.edu/cek7/nyfish/ Сур rinidae/common_carp.jpg Actinop-terygii Neop-terygii Cyprini-formes Cypri-nidae Cyprinus

2* Salmo labrax Pallas Лосось чорноморський (сьомга) Foto 2: https://redbook- ua.org/media/images/ main/a-327.png Actinop-terygii Neop-terygii Salmoni-formes Salmo-nidae Salmo

3* Salmo trutta L. Лосось струмковий (форель струмкова) Foto 3: https:// ih 1. redbubble .net/ image.65006165.3846/ flat,800x800,070,f.u2.jpg Actinop-terygii Neop-terygii Salmoni-formes Salmo-nidae Salmo

Table "Virus"

Primary key Name (Latin) Name (national) Genom Family Dimen-tions Disease it cause Symptoms of disease Image Other

kA VHS virus Virus of haemor-rhagic septicaemia RNA Rhabdo-viridae 180-240x 60-75 nm Viral haemor-rhagic septicaemia, VHS Darkening of the skin and fecundity of the fish. The sick fish slowly floats on the surface of the water, almost does not react to irritation. Gum is anemic, with striped hemorrhages. The hemorrhages are also found in the conjunctiva, darkening of the skin, anorexia, inhibition of fish. The disease runs in acute, chronic and nervous forms. Image 1: https:// en.wikipedia.org/ wiki/ Viral_ hemorrhagic_ septicemia*/ media/File :VHS. png

kB Rhabdo- virus carpió (Spring Viraemia of Carp Virus (SVCV)) Rhabdo-virus carp RNA Rhabdo-viridae 105-125x70-85 nm Spring Viraemia of Carp Virus, SVCV Darkening of the skin, anorexia, inhibition of fish. The disease runs in acute, chronic and nervous forms. Image 2: https:// nas.er. usgs.gov/ XIMAGESERV ERX/2012/ 20120424150715. JPG

Fig. 5. Tables of developed database with information about fishes: a, b — the attributes became the fields of the tables. "Primary keys" are the tools for the formation of the relationships between the tables. Cyrilic words are in tables for domestic use in Ukraine. The link between codes 1* and kB means that carp is infected by "Spring viremia virus" and this will be shown in future database

world were given. Within the framework of the thesis on the correspondence of the hierarchical classification of living objects and the idea of hierarchical subordination of objects in some methods of computer sciences (including databases development), an example of the hierarchical classification of Cyprinus carpio L. carp was given. It was noted that similar type of subordination can be reflected in the attributes in process of relational biological database development, exactly this was done further by the authors.

This publication also provides a description of the concepts of objects, relationships, attributes, keys, and others.

REFERENCES

1. Movchan Yu. V. Fishes of Ukraine (taxonomy, nomenclature, remarks). Collection of works of Zoological Museum. 2009, N 40, P. 47-87.

2. Daoliang Li, Zetian Fu, Yanqing Duan. Fish-Expert: a web-based expert system for fish disease diagnosis. Expert Systems with Applications. 2002, V. 23, P. 311-320.

3. Zeldis D., Prescott S. Fish disease diagnosis program—Problems and some solutions. Aquacultural Engineering. 2000, 23 (1-3), 3-11.

4. Duan Y., Fu Z., Li D. Toward Developing and Using Web-based Tele-Diagnosis in Aquaculture. Expert System ith Applications. 2003, 25 (2), P. 247-254.

5. Smirnov I. S., Voronina E. P., Lobanov A. L., Golikov A. A., Neyelov A. V. Creation of Information retrieval systems on collections of marine animals (fishes and invertebrates) in the Zoological Institute of RAS. Proceedings of the Sixth National Russian Research Conference RCDL'2004, (Pushkino, September 29 — October 1, 2004) Moscow. 2004, P.30-33.

6. Chen Y., Shi M., Cheng Y., Zhang W., Tang Q., Xia Q. FVD: The fish-associated virus database. Infect Genet. 2018, V. 58, P. 26-23. https:// doi.org/10.1016/j.meegid.2017.11.004

7. Crane M., Hyatt A. Viruses of fish: an overview of significant pathogens. Viruses. 2011, 3 (11), 2025-2046. https://doi.org/10.3390/ v3112025

8. Zhang Q., Gui J. F. Virus genomes and virushost interactions in aquaculture animals. Sci. China Life Sci. 2015, 58 (2), P. 156-169. https://doi.org/10.1007/s11427-015-4802-y

9. Valdez-Moreno M., Ivanova N. V., Elias-Gutie rrez M., Pedersen S. L., Bessonov K., Hebert P. D. N. Using eDNA to biomonitor the fish community in a tropical oligotrophic lake. PLoS One. 2019, 14 (4),

In addition, on the example of biological objects, the types of relations 1:1, 1:N, M:N were shown. On the basis of such theoretical generalizations an ER-diagram for two types of objects was developed: for the carp Cyprinus carpio L. and viruses causing diseases of this fish. At the end of the pubication it was shown how relational tables of biological databases are formed, and how they are interconnected with the "keys". The authors hope that present work will be useful for training students of the corresponding specialties, as well as will contribute to the further elaboration of domestic biological databases in Ukraine.

e0215505. https://doi.org/10.1371/journal. pone.0215505

10. Bi H., Zhong C, Shao M, Wang C, Yi J., Qiao L., Zhang J. Differentiation and Authentication of Fishes at Species Level Through Analysis of Fish Skin by MALDI TOF MS. Rapid Commun Mass Spectrom. 2019. https://doi.org/10.1002/rcm.8474

11. Froese R., Pauli D. FishBase 2000: Concepts, designs and data sources. ICLARM. Los Banos, Philippines. 2000, 344 p.

12. User Reference for Fisheries Improvement ProjectsDatabase (FIP-DB) and Query Viewer. URL: https://ru.scribd.com/ document/385739269/Readme-File-for-FIP-DB#download

13. Moreau J., Costa-Pierce B. Introduction and present status of exotic carp in Africa. Aquacult. Res. 1997, V. 28, P. 717-732.

14. FAO. 2016. The State of World Fisheries and Aquaculture 2016. Contributing to food security and nutrition for all. Rome. 200 p. URL: http://www.fao.org/3/a-i5555e.pdf

15. Julie M. Roessig, Christa M. Woodley, Joseph J. Cech, Jr., Lara J. Hansen. Effects of Global Climate Change on Marine and Estuarine Fishes and Fisheries. Springer. 2004, 344 p.

16. A Global Information System on Fishes. https://www.fishbase.se/home.htm

17. Arnot J. A., Mackay D, Parkerton T. F., Bon-nell M. A database of fish biotransformation rates for organic chemicals. Environmental Toxicology and Chemistry. 2008, 27 (11), P. 2263-2270. URL: https://setac. onlinelibrary.wiley.com/doi/full/10.1897/08-058.1#accessDenialLayout. https://doi. org/10.1897/08-058.1; https://www.ncbi. nlm.nih.gov/pubmed/18522452

18. Tedesco P. A., Beauchard O, Bigorne R., Blanchet S., Buisson L., Conti L., Cornu J.-F.,

Dias M. S.,, Grenouillet G., Hugueny B., Jezequel C., Leprieur F., Brosse S., Oberdorff T. A global database on freshwater fish species occurrence in drainage basins. Sci. Data. 2017, V. 4, P. 170141. https:// doi.org/10.1038/sdata.2017.141 https://figshare.com/collections/A_ global_database_on_freshwater_fish_ species_occurrences_in_drainage_ basins/3739145/1

19. Van der Laan R., W. N. Eschmeyer & R. Fricke Family-group names of Recent fishes. Zootaxa Monograph. 2014 (11 Nov.), 3882 (1), P. 1-230. https://doi.org/10.11646/ zootaxa.3882.1.1

20. Fricke R., Eschmeyer W. N. & R. van der Laan. Eschmeyer's catalog of fishes: genera, species, 2019. http://researcharchive.calacademy. org/research/ichthyology/index.php

21. Maistrenko M. I., Rud Yu. P., Buchatsky L. P. Accumulation of IPNV on the culture of fish cells. Animal Biology. 2014, 16 (4), P. 93-99. (In Ukrainian).

22. Matvienko N. M., Buchatsky L. P. The use of immunomodulating preparations in fish farming. In book: Modern problems of theoretical and practical ichthyology. 2014, P. 33-35. (In Ukrainian).

23. Gavrilova I. P., Maistrenko M. I., Rumar V. I., Buchatsky L. P., Rud Yu. P. New hosts of the third-type of carp herpes virus (CyHV-3). Scientific notes of the Ternopil Ped. University. Series Biology. 2014, 58 (1), P. 16-20. (In Ukrainian).

24. Maistrenko M. I., Buchatsky L. P. Biology of Herpesvirus Fish. Problems of ecological and medical genetics and clinical immunology. Lugansk. 2014, Is. 3 (123), P. 19-35. (In Ukrainian).

25. Rud Yu. P., Maistrenko M. I., Bezusy O. L., Buchatsky L. P. Experimental infection of long-winded cancer (Pontastacus leptodactylus) with virus of infectious pancreatic necrosis. Bulletin of Biology and Medicine. 2014, Is. 4, 1 (113), P. 70-74. (In Ukrainian).

26. Matvienko N., Rud Yu., Buchatsky L. Replication of Infectious Pancreatic Necrosis Virus in different cell lines and organism of rainbow trout (Oncorhynchus mykiss) fingerlings. Arch. Pol. Fish. 2014, V. 22, P. 127-133.

27. Matvienko N. M., Buchatsky L. P. Study of reproduction of hemorrhagic septicemia trout virus. Bulletin of Problems of Biology and Medicine. 2014, Is. 2, V. 3, P. 118-121. (In Russian).

28. Matvienko N. M,, Buchatsky L. P., Deriabin O. M. Application of a reverse transcriptase polymerase chain reaction for the detection and identification of the virus of infectious

pancreatic necrosis of rainbow trout (Oncorhynchus mykiss). Microbiology and biotechnology. 2013, 4 (24), 46-54. (In Ukrainian).

29. Matvienko N., Kharkavlyuk N., Buchatsky L., Didenko A. Characteristics of spring viraemia of carp virus strains isolated in different regions of Ukraine. Zoology and Ecology. 2013, V. 23, Is. 3, Р. 198-202.

30. Matvienko N., Rud Y., Buchatsky L. Replication of infectious pancreatic necrosis virus in different cell lines and organism of rainbow trout (Oncorhynchus mykiss) fingerlings. Arch. Polish Fisheries. 2013,N 4.

31. Rud Yu. P., Maistrenko M. I., Buchatsky L. P. Amplification and analysis of the nucleotide sequence of VP2 TA NS IPNV genes isolated in Western Ukraine. Problems of ecological and medical genetics and clinical immunology. 2013, Is. 4 (118), P. 34-40. (In Ukrainian).

32. Maistrenko M. I., Buchatsky L. P., Matvienko N. M. Strain of herpesvirus 1MB В-4 for the obtaining a vaccine against herpes virus koi. Patent UA. Bull. No. 8, Issued: 13. 05. 2013. (In Ukrainian).

33. Rud Yu., Maistrenko M., Buchatsky L. Isolation of IPNV from wild-life rainbow trout (Oncorhynchus mykiss) in western Ukraine. Biology. 2013, 3 (65), P. 63-65.

34. Maistrenko M. I., Rud Yu. P., Matvienko N. M, Cholodna L. S., Buchatsky L. P. Identification of virus SyNV-3 by the methods of electron microscopy and polymerase chain reaction. Reports of the National Academy of Sciences of Ukraine. 2013, No 4, P. 139-143. (In Ukrainian).

35. Matvienko N. M., Vashchenko А. V. Tsiga-nok I. O., Buchatsky L. P. Results of surveillance studies of infectious fish diseases in freshwater aquaculture of Ukraine. Agricultural science and practice. 2015, 2 (2), P. 32-38.

36. Rud Y. P., Buchatsky L. P. Detection of infectious pancreatic necrosis virus in the western Ukraine. Virologica sinica. 2015, V. 30, Is. 2, P. 1-4.

37. Klyuchko O. M. Information and computer technologies in biology and medicine. Kyiv: Nat. Acad. Scienc. Ukraine-druk. 2008, 252 p. (In Ukrainian).

38. Klyuchko O. М. On the mathematical methods in biology and medicine. Biotechnol. acta. 2017, 10 (3), P. 31-40. https://doi. org/10.15407/biotech10.03.031

39. Klyuchko O. М. Application of artificial neural networks method in biotechnology. Biotechnol. acta. 2017, 10 (4), P. 5-13. https://doi.org/10.15407/ biotech10.04.005

40. Klyuchko O. M. Cluster analysis in biotechno logy. Biotechnol. acta. 2017, 10 (5), P. 5-18. https://doi.org/10.15407/biotech10.05.005

41. Klyuchko O. M. Technologies of brain images processing. Biotechnol. acta. 2017,

10 (6), 5-17. https://doi.org/10.15407/ biotech10.05.005

42. Klyuchko O. M., Onopchuk Yu. M. Some trends in mathematical modeling for biotechnology. Biotechnol. acta. 2018,

11 (1), P. 39-57. https://doi.org/10.15407/ biotech11.01.039

43. Klyuchko O. M. Electronic information systems in biotechnology. Biotechnol. acta. 2018, 11 (2), 5-22. https://doi. org/10.15407/biotech11.02.005

44. Klyuchko O. M. Information computer technologies for biotechnology: electronic medical information systems. Biotechnol. acta. 2018, 11 (3), P. 5-26. https://doi. org/10.15407/biotech11.03.005

45. Klyuchko O. M, Klyuchko Z. F. Electronic databases for Arthropods: methods and applications. Biotechnol. acta. 2018, 11 (4), P. 28-49. https://doi.org/10.15407/ biotech11.04.028

46. Klyuchko O. M., Klyuchko Z. F. Electronic information systems for monitoring of populations and migrations of insects. Biotechnol. acta. 2018, 11 (5), P. 5-25. https://doi.org/10.15407/ biotech11.05.005

47. Klyuchko O. M. Expert systems for biology and medicine. Biotechnol. acta. 2018, 11 (6), P. 5-28. https://doi.org/10.15407/ biotech11.06.005

48. Klyuchko O. M. Biotechnical information systems for monitoring of chemicals in environment: biophysical approach. Biotechnol. acta. 2019, 12 (1), P. 5-28.

49. Klyuchko O. M, Aralova N. I., Aralova A. A. Electronic automated work places for biological investigations. Biotechnol. acta. 2019, 12 (2), P. 5-22.

50. Kolomiychuk V. P., Melezhyk O. V. Plant syntaxonomy of landslide shores of Azov Sea. Bulletin of Zaporizhzhia National University. Biological sciences. 2014, N 1, P. 173-182.

51. Melezhyk O. V. Estimation of the state of memorial parks of garden art in Kyiv city. Ecological Sciences. 2016, N 13-14, P. 110-122.

52. Sachnyuk G. V., Melezhyk O. V. Current state of water resources protection against pollution. Youth: education, science, spirituality: theses of reports XV Ukr. Sci. conf. Kyiv, April 17-19, 2007. P. 409-410.

53. Schnase J. L, Cushing J., Frame M. Information technology challenges of biodiversity and ecosystems informatics. Inform. syst. 2003, 28 (4), P. 339-345.

54. Hardy P. B., Sparks T. H, Isaak N. J. Specialism for larval and adult consumer resources among Brittish butterflies: implications for conservation. Biol. Conserv. 2007, 138 (3-4), P. 440-452.

55. Dennis R. L. H., Shreeve T. G., Sparks T. H. A comparison of geographical and neighbourhood models for improving atlas databases. The case of the French butterfly atlas. Biol. Conserv. 2002, 108 (2), P. 143-159.

56. Plakhotnij S. A., Klyuchko O. M., Krotinova M. V. Information support for automatic industrial environment monitoring systems. Electr. Contr. Syst. 2016, 1 (47), 1 P. 9-34.

57. Onopchuk Yu. M., Aralova N. I., Klyuchko O. M., Beloshitsky P. V. Mathematic models and integral estimation of organism systems reliability in extreme conditions. Electr. Contr. Syst. 2015, 4 (46), P. 109-115.

58. Onopchuk Yu. M., Aralova N. I., Klyuchko O. M., Beloshitsky P. V. Integral estimations of human reliability and working capacity in sports wrestling. J. Engin. Acad. 2015, V. 3, P. 145-148. (In Russian).

59. Klyuchko O. M,, Shutko V.N., Navrotskyi D. O, Mikolushko A. M. The set of program models for ecological monitoring technical system based on principles of biophysics. Electr. Contr. Syst. 2014, 4 (42), P. 135-142.

60. Klyuchko O. M., Sheremet D. Yu. Computer simulation of biological nanogenerator functions. Electr. Contr. Syst. 2014, 2 (40), P.103-111.

61. Klyuchko O. M., Shutko V. N. Computer modeling of auto-oscillating phenomena in neuron complexes. Electr. Contr. Syst. 2014, 1 (39), P. 127-132.

62. Klyuchko O. M., Sheremet D. Yu. Computer modeling of biologic voltage-activated nanostructures. Electr. Contr. Syst. 2014, 1 (39), P. 133-139.

63. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. M. Radiation damage of organism and its correction in conditions of adaptation to highmountain meteorological factors. Bull. NASU. 2010, N. 1, P. 224-231. (In Ukrainian).

64. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. Results of research of structural and functional interdependencies by Ukrainian scientists in Prielbrussie. Bull. NASU. 2009, N 1, P. 61-67. (In Ukrainian).

65. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. Results of research of highlands factors influence on health and longevity by Ukrainian scientists in Prielbrussie. Bull. NASU. 2008, N 4, P. 108-117. (In Ukrainian).

66. Onopchuk Yu. M., Klyuchko O. M., Beloshits-ky P. V. Development of mathematical models basing on researches of Ukrainian scientists at Elbrus. Bull. NASU. 2008, N 3, P. 146-155. (In Ukrainian).

67. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. Results of research of adaptation problems by Ukrainian scientists in Prielbrussie. Bull. NASU. 2008, N 1, P. 102-108. (In Ukrainian).

68. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. Results of research of hypoxia problems by Ukrainian scientists in Elbrus region. Bull. NASU. 2007, N 3-4, P. 44-50. (In Ukrainian).

69. Beloshitsky P. V., Klyuchko O. M., Onopchuk Yu. Results of medical and biological research of Ukrainian scientists at Elbrus. Bull. NASU. 2007, N 2, P. 10-16. (In Ukrainian).

70. Linne C. Fauna Suecica. Stocholmiac. 1761, 578 p.

71. Klyuchko Z. F. Noctuidae of the Western regions of Ukraine. Kyiv: Kyiv Univ. Publ. 1963, 176 p. (In Russian).

72. Berks G., Ghassemi A., von Keyserlingk D. G. Spatial registration of digital brain atlases based on fuzzy set theory. Comp. Med. Imag. Graph. 2001, 25 (1), P. 1-10. https://doi. org/10.1016/S0895-6111(00)00038 0

73. Nowinski W. L., Belov D. The Cerefy Neuroradiology Atlas: a Talairach-Tournoux atlas-based tool for analysis of neuroimages available over the internet. NeuroImage. 2003, 20 (1), P. 50-57. https://doi. org/10.1016/S1053-8119(03)00252-0

74. Chaplot S., Patnaik L. M., Jagannathan N. R. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed. Signal Proc. Contr. 2006, 1 (1), P. 86-92. https://doi.org/10.1016Zj.bspc.2006.05.002

75. Vecht-Lifshitz S. E., Ison A. P. Biotechnological applications of image analysis: present and future prospects. J. Biotechnol. 1992, 23 (1), P. 1-18.

76. Carro S. A., Scharcanski J. A framework for medical visual information exchange on the WEB. Comput. Biol. Med. 2006, V. 4, P. 327-338.

77. Dikshit A., Wu D., Wu C., Zhao W. An online interactive simulation system for medical imaging education. Comp. Med. Imag. Graph. 2005, 29 (6), P. 395-404. https://doi. org/10.1016/j.compmedimag.2005.02.001

78. Singh R., Schwarz N., Taesombut N., Lee D., Jeong B., Renambot L., Lin A. W., West R., Otsuka H., Naito S., Peltier S. T., Martone M. E., Nozaki K., Leigh J., Ellisman M. H. Real-time multi-scale brain data acquisition, assembly, and analysis using an end-to-end. OptIPuter Fut. Gener. Comput. Syst. 2006, V. 22, P. 1032-1039.

79. Klyuchko O. M. Using of images' databases for diagnostics of pathological changes in organism tissues. Electr. Contr. Syst. 2009, 2 (20), P. 62-68. (In Ukrainian).

80. Steimann F. On the representation of roles in object-oriented and conceptual modelling. Data Know. Engin. 2000, 35 (1), P. 83-106.

81. Klyuchko O. M., Managadze Yu. L., Pashkivsky A. O. Program models of 2D neuronal matrix for ecological monitoring and images' coding. Bull. NASU. 2013, N 3-4, P. 77-82. (In Ukrainian).

82. Klyuchko O.M,, Piatchanina T. V., MazurM. G. Combined use of relation databases of images for diagnostics, therapy and prognosis of oncology diseases. "Integrated robototechnic complexes". X IIRTC-2017 Conference Proceedings. 2017, P. 275-276. (In Ukrainian).

83. Shutko V. M., Shutko O. M., Kolganova O. O. Methods and means of compression of information. Kyiv: Nauk. dumka. 2012, 168 p. (In Ukrainian).

84. Jecheva V., Nikolova E. Some clusteringbased methodology applications to anomaly intrusion detection systems. Int. J. Secur. Appl. 2016, 10 (1), P. 215-228. http:// dx.doi.org/10.14257/ijsia.2016.10.1.20

85. Iakovidis D. K., Maroulis D. E., Karkanis S. A. Texture multichannel measurements for cancer precursors' identification using support vector machines. Measurement. 2004, V. 36, P. 297-313. https://doi. org/10.1016/j.measurement

86. Nguyen H. Q., Carrieri-Kohlman V., Rankin S. H., Slaughter R, Stulbarg M. S. Internet-based patient education and support interventions: a review of evaluation studies and directions for future research. Comp. Biol. Med. 2004, 34 (2), P. 95-112. https:// doi.org/10.1016/S0010-4825(03)00046-5

87. Jézéquel P., Loussouarn L., Guérin-Charbon-nel C., Campion L., Vanier A., Gouraud W., Lasla H., Guette C., Valo I., Verrièle V. Campone M. Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response. Breast Cancer Res. 2015, 17 (1), 43 p. https://doi.org/10.1186/s13058-015-0550-y

88. Onopchuk Yu. M., Biloshitsky P. V., Klyuch-ko O. M. Development of mathematical models based on the results of researches of Ukrainian scientists at Elbrus. Bull. NASU. 2008, N 3, P. 146-155. (In Ukrainian).

89.Ankur Poudel, Dhruba Bahadur Thapa, Manoj Sapkota. Cluster Analysis of Wheat (Triticum aestivum L.) Genotypes Based Upon Response to Terminal Heat Stress. Int. J. Appl. Sci. Biotechnol. 2017, 5 (2), P. 188193. https://dx.doi.org/10.3126/ijasbt. v5i2.17614

90. Zaslavsky L., Ciufo S., Fedorov B., Tatusova T. Clustering analysis of proteins from microbial genomes at multiple levels of resolution. BMC Bioinform. 2016, 17 (8), 276 p. Published online 2016 Aug 31. https://doi. org/10.1186/s12859-016-1112-8

91. Zhou J., Richardson A. J., Rudd K. E. EcoGene-RefSeq: EcoGene tools applied to the RefSeq prokaryotic genomes. Bioinformatics. 2013, 29 (15), P. 1917-1918. Published: 04 June 2013. https://doi.org/10.1093/ bioinformatics/btt302

92. Zhang J., Wu G., Hu X., Li S., Hao S. A Parallel Clustering Algorithm with MPI — MKmeans. J. Comput. 2013, 8 (1), 1017 p. https://doi.org/10.1109/PAAP.2011.17

93. Tatusova T., Zaslavsky L., Fedorov B., Had-dad D., Vatsan A., Akoadjei D., Blinkova O., Ghazal H. Protein Clusters. The NCBIHandbook [Internet]. 2nd edition. Available at www.ncbi.nlm.nih.gov/books/ NBK242632

94. Anderson J. G. Evaluation in health informatics: computer simulation. Comput. Biol. Med. 2002, 32 (3), P. 151-164. https:// doi.org/10.1016/S0010-4825(02)00012-4

95. Aruna P., Puviarasan N., Palaniappan B. An investigation of neuro-fuzzy systems in psychosomatic disorders. Exp. Syst. Appl. 2005, 28 (4), 673-679. https://doi. org/10.1016/j.eswa.2004.12.024

96. Bange M. P., Deutscher S. A., Larsen D., Linsley D., Whiteside S. A handheld decision support system to facilitate improved insect pest management in Australian cotton systems. Comp. Electron. Agricult. 2004, 43 (2), 1 P. 31-147. https://doi. org/10.1016/j.compag.2003.12.003

97. Beaulieu A. From brainbank to database: the informational turn in the study of the brain. Stud. Hist. Phil. Biol. Biomed. Sci. 2004, V. 35, P. 367-390. https://doi. org/10.1016/j.shpsc.2004.03.011

98. Horn W. AI in medicine on its way from knowledge-intensive to data-intensive systems. Artif. Intell. Med. Elsevier. 2001, 23 (1), 512 p. https://doi.org/10.1016/ S0933-3657(01)00072-0

99. Brake I. Unifying revisionary taxonomy: insect exemplar groups. Abstr. XV SEL Congr. Berlin (Germany). 2007.

100. Braxton S. M., Onstad D. W., Dockter D. E., Giordano R., Larsson R., Humber R. A. Description and analysis of two internetbased databases of insect pathogens: EDWIP and VIDIL. J. Invertebr. Pathol. 2003, 83 (3), P. 185-195. https://doi. org/10.1016/S0022-2011(03)00089-2

101. Breaux A., Cochrane S., Evens J., Martindaled M., Pavlike B., Suera L., Benner D. Wetland ecological and

compliance assessments in the San Francisco Bay Region, California, USA. J. Environm. Manag. 2005, 74 (3), P. 217-237.

102. Budura A., Cudre-Mauroux P., Aberer K. From bioinformatic web portals to semantically integrated Data Grid networks. Fut. Gener. Comput. Syst. 2007, 23 (3), P. 281-522. https://doi.org/10.1016/j. jenvman.2004.08.017

103. Burns G., Stephan K. E., Ludäscher B., Gupta A., Kö tter R. Towards a federated neuroscientific knowledge management system using brain atlases. Neurocomputing. 2001, V. 3840, P. 16331641. https://doi.org/10.1016/S0925-2312(01)00520-3

104. Butenko S., Wilhelm W. E. Clique-detection models in computational biochemistry and genomics. Eur. J. Oper. Res. 2006, 173 (1), 117. https://doi.org/10.1016/j. ejor.2005.05.026

105. Carro S. A., Scharcanski J. Framework for medical visual information exchange on the WEB. Comp. Biol. Med. 2006, 36 (4), P. 327-338. https://doi.org/10.1016/j. compbiomed.2004.10.004

106. Chau M., Huang Z., Qin J., Zhou Y., Chen H. Building a scientific knowledge web portal: The NanoPort experience. Dec. Sup. Syst. 2006. https://doi.org/10.1016/j. dss.2006.01.004

107. Chen M., Hofestädt R. A medical bioinformatics approach for metabolic disorders: Biomedical data prediction, modeling, and systematic analysis. J. Biomed. Inform. 2006, 39 (2), 1, P. 47-159. https://doi.org/10.1016Zj.jbi.2005.05.005

108. Chli M., De Wilde P. Internet search: Subdivision-based interactive query expansion and the soft semantic web. Appl. Soft Comput. 2006. https://doi. org/10.1016/j.asoc.2005.11.003

109. Despont-Gros C., Mueller H., Lovis C. Evaluating user interactions with clinical information systems: A model based on human — computer interaction models. J. Biomed. Inform. 2005, 38 (3), P. 244-255. https://doi.org/10.1016/j.jbi.2004.12.004

110. Marios D., Dikaiakos M. D. Intermediary infrastructures for the World Wide Web. Comp. Networks. 2004, V. 45, P. 421-447. https://doi.org/10.1016/j. comnet.2004.02.008

111. Dimitrov S. D., Mekenyan O. G., Sinks G. D, Schultz T. W. Global modeling of narcotic chemicals: ciliate and fish toxicity. J. Mol. Struc.: Theochem. 2003, 622 (12), P. 63-70. https://doi.org/10.1016/S0166-1280(02)00618-8

112. Dong Y., Zhuang Y., Chen K., Tai X. A hierarchical clustering algorithm based

on fuzzy graph connectedness. Fuzzy Sets. Syst. 2006, V. 157, P. 1760-1774. https:// doi.org/10.1016/j.fss.2006.01.001

113. Duan Y, Edwards J. S, Xu M. X. Web-based expert systems: benefits and challenges. Inf. Manag. 2005, 42 (6), P. 799-811. https:// doi.org/10.1016/j.im.2004.08.005

114. Essen van D. C. Windows on the brain: the emerging role of atlases and databases in neuroscience. Curr. Opin. Neurobiol. 2002, 12 (5), P. 574-579. https://doi. org/10.1016/S0959-4388(02)00361-6

115. Fellbaum C., Hahn U., Smith B. Towards new information resources for public health From Word Net to Medical Word Net. J. Biomed. Inform. 2006, 39 (3), P. 321-332. https://doi.org/10.1016Zj.jbi.2005.09.004

116. Ferraris M., Frixione P., Squarcia S. Network oriented radiological and medical archive. Comp. Physics Commun. 2001, V. 140, P. 226-232. https://doi.org/10.1016/ S0010-4655(01)00273-9

117. Flower D. R., Attwood T. K. Integrative bioinformatics for functional genome annotation: trawling for G protein-coupled receptors. Reviews 55 Semin. Cell. Dev. Biol. 2004, 15 (6), P. 693-701. https://doi. org/10.1016/j.semcdb.2004.09.008

118. Fink E., Kokku P. K., Nikiforou S., Hall L. O., Goldgof D. B., Krischer J. P. Selection of patients for clinical trials: an interactive webbased system. Art. Intell. Med. 2004, 31 (3), P. 241-254. https://doi. org/10.1016/j.artmed.2004.01.017

119. Fitzpatrick M. J., Ben-Shahar Y., Smid H. M., Vet L. E., Robinson G. E., Sokolowski M. B. Candidate genes for behavioural ecology. Trend Ecol. Evol. 2005, 20 (2), P. 96-104. https://doi.org/10.1016/j. tree.2004.11.017

120. Fox J., Alabassi A., Patkar V., Rose T., Black E. An ontological approach to modelling tasks and goals. Comp. Biol. Med. 2006, V. 36, P. 837-856. https://doi.org/10.1016/j. compbiomed.2005.04.011

121. Fu Zetian, Xu Feng, Zhou Yun, Shuan X. Z. Pig-vet: a web-based expert system for pig disease diagnosis. 2006. https://doi. org/10.1016/j.eswa.2005.01.011

122. Gaulton A., Attwood T. K. Bioinformatics approaches for the classification of G-protein-coupled receptors. Curr. Opin. Pharmacol. 2003, 3 (2), 114-120. https:// doi.org/10.1016/S1471-4892(03)00005-5

123. Palla K., Ghahramani Z., Knowles D. A. A nonparametric variable clustering model. Adv. Neur. Inform. Proc. Syst. 2012, P. 2987-2995.

124. Curtis C. Sohrab P. Shah, Suet-Feung Chin, Gulisa Turashvili, Oscar M. Rueda, Mark J. Dunning, Doug Speed, Andy G.

Lynch, Shamith Samarajiwa, Yinyin Yuan, Stefan Graf, Gavin Ha, Gholamreza Haffari, Ali Bashashati, Roslin Russell, Steven McKinney, METABRIC Group, Anita Langerod, Andrew Green, Elena Provenzano, Gordon Wishart, Sarah Pinder, Peter Watson, Florian Markowetz, Leigh Murphy, Ian Ellis, Arnie Purushotham, Anne-Lise Borresen-Dale, James D. Brenton, Simon Tavare, Carlos Caldas, Samuel Aparicio. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012, 486 (7403), P. 346-352.

125. Proceedings. Of the second EuroLOOP Workshop, Amsterdam, 27-30 March 1995.

126. Graham C. H., Ferrier S., Huettman F., Moritz C., Peterson A. T. New developments in museum-based informatics and applications in biodiversity analysis. Trend. Ecol. Evol. 2004, 19 (9), P. 497-503. https://doi.org/10.1016/j. tree.2004.07.006

127. Anokhin B., Hemmrich G. Zoology www guide. Zoology. 2005, 108 (4), P. 673-679.

128. Hirano S., Sun X., Tsumoto S. Comparison of clustering methods for clinical databases. Inform. Sci. 2004, 159 (34), P. 155-165. https://doi.org/10.1016Zj.ins.2003.03.011

129. Hong Yu., Hatzivassiloglou V., Rzhetsky A., Wilbur W. J. Automatically identifying gene/protein terms in MEDLINE abstracts. J. Biomed. Inform. 2002, 35 (56), P. 322330. https://doi.org/10.1016/S1532-0464(03)00032-7

130. Bedathur S. J., Haritsa J. R., Sen U. S. The building of BODHI, a bio-diversity database system. Inform. Syst. 2003, 28 (4), P. 347367. https://doi.org/10.1016/S0306-4379(02)00073-X

131. Hsi-Chieh Lee, Szu-Wei Huang, Li E. Y. Mining protein-protein interaction information on the internet. Exp. Syst. Appl. Elsevier. 2006, 30 (1), P. 142-148. https:// doi.org/10.1016/j.eswa.2005.09.083

132. Young R. Genetic toxicology: web-resources. Toxicology. 2002, 173 (1-2), P. 103-121.

133. Johnson S. B., Friedman R. Bridging the gap between biological and clinical informatics in a graduate training program. J. Biomed. Inform. 2007, 40 (1), P. 59-66. Epub. 2006 Mar 15. https://doi.org/10.1016/ j.jbi.2006.02.011

134. Kaiser M., Hilgetag C. C. Modelling the development of cortical systems networks. Neurocomputing. 2004, V. 5860, P. 297-302. https://doi.org/10.1016/j. neucom.2004.01.059

135. Kane M. D., Brewer J. L. An information technology emphasis in biomedical informatics education. J. Biomed. Inform.

2007, 40 (1), P. 67-72. https://doi. org/10.1016/j.jbi.2006.02.006

136. Kannathal N., Acharya U. R., Lim C. M., Sadasivan P. K. Characterization of EEG. A comparative study. Comp. Meth. Progr. Biomed. 2005, 80 (1), P. 17-23. https://doi. org/10.1016/j.cmpb.2005.06.005

137. Koh W., McCormick B. H. Brain microstructure database system: an exoskeleton to 3D reconstruction and modelling. Neurocomputing. 2002, V. 4446, P. 1099-1105. https://doi.org/10.1016/ S0925-2312(02)00426-5

138. Koh W., McCormick B. H. Registration of a 3D mouse brain atlas with brain microstructure data. Neurocomputing. 2003, V. 5254, P. 307-312. https://doi. org/10.1016/S0925-2312(02)00793-2

139. Kulish V., Sourin A., Sourina O. Human electro encephalograms seen as fractal time series: Mathematical analysis and visualization. Comp. Biol. Med. 2006, 36 (3), P. 291-302. https://doi.org/10.1016/j. compbiomed.2004.12.003

140. Lubitz von D., Wickramasinghe N. Networkcentric healthcare and bioinformatics: Unified operations within three domains of knowledge. Exp. Syst. Appl. 2006, 30 (1), P. 11-23. https://doi. org/10.1016/j.eswa.2005.09.069

141. Martin-Sanchez F., Iakovidis I., Norager S., Maojo V., de Groen P., Van der Lei J., Jones T, Abraham-Fuchs K., Apweiler R., Babic A., Baud R., Breton V., Cinquin P., Doupi P., Dugas M., Eils R., Engelbrecht R., Ghazal P., Jehenson P., Kulikowski C., Lampe K., De Moor G., Orphanoudakis S., Rossing N., Sarachan B., Sousa A., Spekowius G., Thireos G., Zahlmann G., Zvárová J., Hermosilla I., Vicente F. J. Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care. J. Biomed. Inform. 2004, 37 (1), P. 30-42. https://doi.org/10.1016Zj.jbi.2003.09.003

142. Masseroli M., Visconti A., Bano S. G, Pinciroli F. He@lthCo-op: a web-based system to support distributed healthcare co-operative work. Comp. Biol. Med. 2006, 36 (2), P. 109-127. https://doi. org/10.1016/j.compbiomed.2004.09.005

143. Moon S., Byun Y., Han K. FSDB: A frameshift signal database. Comp. Biol. Chem. 2007, 31 (4), P. 298-302. https://doi. org/10.1016/j.compbiolchem.2007.05.004

144. Orgun B., Vu J. HL7 ontology and mobile agents for interoperability in heterogeneous medical information systems. Comp. Biol. Med. 2006, 36 (78), 817-836. https://doi. org/10.1016/j.compbiomed.2005.04.010

145. Pérez-Rey D., Maojo V., García-Remesal M., Alonso-Calvo R., Billhardt H., Mar-

tin-Sànchez F., Sousa A. Ontofusion: Ontologybased integration of genomic and clinical databases. Comp. Biol. Med. 2006, 36 (78), 712-730. https://doi. org/10.1016/j.compbiomed.2005.02.004

146. Krishtal O. A., Kiskin N. I., Tsyndrenko A Ya., Klyuchko E. M. Pharmacological properties of amino acid receptors in isolated hippocampal neurons. In: Receptors and ion channels. Ed. by Ovchinnikov Y. A., Hucho F. Berlin-New York: Walter de Gruyter. 1987, P. 127-137.

147. Klyuchko E. M., Klyuchko Z. F., Beloshitsky P. V. Some adaptation characteristics of insects in mountains of Prielbrussie. Nalchik (Russia), "Hypoxia: automatic analysis of hypoxic states of healthy people and sick ones". 2005, V. 1, P. 137-140. (In Russian).

148. Klyuchko Z. F., Klyuchko E. M. Analysis of taxonomic structure of moth fauna (Lepidoptera: Noctuidae s.l.) of Ukraine according to monitoring data. Eversmannia. 2012, 3 (33), 41-45. (In Russian).

149. Klyuchko Z. F., Klyuchko E. M. Moth (Lepidoptera: Noctuidae s. l.) of Chercasska region of Ukraine according to results of many-year monitoring. Eversmannia. 2014, V. 37, P. 32-49. (In Russian).

150. Gonchar O., Klyuchko O., Seredenko M., Oliynyk B. Correction of metabolic disorders at hypoxia by new pharmacological preparations. Mat. 3 FEPS Congress. Nice (France). 2003, P. 228.

151. Seredenko M., Gonchar O., Klyuchko O., Oliynyk S. Peculiarities of prooxidant — antioxidant balance of organism under hypoxia of different genesis and its corrections by new pharmacological preparations. Acta Physiologica Hungarica. Budapest (Hungary). 2002, 89 (1-3), 292.

152. Klyuchko O., Klyuchko Z., Lizunova A. Electronic Noctuidae database: some problems and solutions. Proc. 16th Europ. Congr. Lepidopterology. Cluj (Romania). 2009, P.31-32.

153. Klyuchko O., Klyuchko Z., Lizunova A. Noctuidae fauna of Ukrainian Karpathy: results of monitoring (1956-2008). Proc. 16th Europ. Congr. Lepidopterology. Cluj (Romania). 2009, P. 31.

154. Klyuchko O. M., Beloshitsky P. V. Investigation of insect adaptation characteristics in Prielbrussie in 20042005. Mat. VIII World Congr. Int. Soc. Adaptive Medicine (ISAM). Moskva (Russia). 2006, Р. 165-166.

155. Beloshitsky P. V., Klyuchko O. M. Contribution of Sirotinin's school into adaptation medicine. Mat. VIII World Congr. Int. Soc. Adaptive Medicine (ISAM). Moskva (Russia). 2006, Р. 158.

156. Klyuchko O. M., Klyuchko Z. F. Ukrainian Noctuidae Database. Mat. XIV SEL Congr. Roma (Italy). 2005, P. 49.

157. Klyuchko Z. F., Klyuchko O. M. Noctuidae (Lepidoptera) of Donbass, Ukraine. Mat. XIVSEL Congr. Roma (Italy). 2005, P. 4142.

158. Beloshitsky P., Klyuchko O., Onopchuck Yu., Onopchuck G. Mathematic model for hypoxic states development for healthy people and ones with ischemic heart disease. High altitude medicine and biology: Mat. ISMM Congr. Beijing (China). 2004, V. 5, P. 251.

159. Beloshitsky P., Klyuchko O., Kostyuk O., Beloshitsky S. Peculiarities of high mountain factors influence on organism. High altitude medicine and biology: Mat. ISMM Congr. Beijing (China). 2004, V. 5, P. 250.

160. Klyuchko O. M., Kiskin N. I., Krishtal O. A., Tsyndrenko A. Ya. Araneidae toxins as antagonists of excitatory amino acid responses in isolated hippocampal neurons. X School on biophysics of membrane transport. Szczyrk (Poland). 1990, V. 2, P. 271.

161. Akaike N., Kawai N., Kiskin N. I., Krish-tal O. A., Tsyndrenko A. Ya., Klyuchko O. M. Spider toxin blocks excitatory amino acid responses in isolated hippocampal pyramidal neurons. Neurosci. Lett. 1987, V. 79, P. 326-330.

162. Gonchar O., Klyuchko O., Beloshitsky P. Ways of myocardial metabolic disorders correction at hypoxia by new pharmacological preparations. High altitude medicine and biology: Mat. ISMM Congr. Beijing (China). 2004, V. 5, P. 249.

163. Gonchar O., Klyuchko E., Mankovskaya I. Role of complex nucleosides in the reversal of oxidative stress and metabolic disorders induced by acute nitrite poisoning. Ind. J. Pharmac. 2006, 38, V. 6, 414-418.

164. Gonchar O., Klyuchko E., Seredenko M., Oliynik S. Corrections of prooxidant — antioxidant homeostasis of organism under hypoxia of different genesis by yackton, new pharmacological preparation. Acta Physiol. Pharmacol. Bulg. 2003, V. 27, P. 53-58.

165. Klyuchko O. M., Tzal-Tzalko V. I. Elaboration of new monitoring system for Ukrainian Polissia conditions with data defense. Bull. Engin. Acad. 2014, V. 2, P. 239-246. (In Ukrainian).

166. Klyuchko O. M., Piatchanina T. V., Ma-zur M. G., Basarak O. V. Ontological methods in the development of biomedical information systems. "Integrated intellectual robototechnical complexes" - "IIRTC-2018": Mater. XI Int. Sci. Tech. Conf. 2018, P. 270-272. (In Ukrainian).

167. Klyuchko O. M., Piatchanina T. V., Mazur M. G., Basarak O. V. Elaboration of networkbased biomedical systems with databases. "Integrated intellectual robototechnical complexes" - "IIRTC-2018": Mater. XI Int. Sci. Tech. Conf. 2018, P. 273-274. (In Ukrainian).

168.Aralova N. I., Klyuchko O. M. Mathematic modeling of functional self-organization of pilots' respiratory systems. "Integrated intellectual robototechnical complexes" - "IIRTC-2018": Mater. XIIntl. Sci. Tech. Conf. 2018, P. 268-269. (In Ukrainian).

169. Klyuchko Z. F., Klyuchko O. M. Moths (Lepidoptera: Noctuidae s. l.) of Cherkasska Region of Ukraine according to results of long-term monitoring. Eversmannia. 2014, V. 37, P. 32-49. (In Russian).

170. Klyuchko Z. F., Klyuchko O. M. Analysis of moth fauna taxonomic structure (Lepidoptera: Noctuidae s. l.) of Ukraine according to monitoring data. Eversmannia. 2014, 33 (3), 41-45. (In Russian).

171. Klyuchko Z. F., Klyuchko O. M. Monitoring of the diversity of Noctuidae (Lepidoptera) fauna in Ukrainian Polissia. "Nature of Polissia: investigation and protection": Mater. Intl. Sct. Conf. 2014, P. 498-502. (In Ukrainian).

172. Klyuchko Z. F., Klyuchko O. M., Lizunova A G. Monitoring of Noctuidae fauna of Ukraine using electronic information systems. "Nature of Polissia: investigation and protection": Mater. Intl. Sci. Conf. 2014, P. 261-265. (In Ukrainian).

173. Klyuchko O. M., Biletsky A. Ya., Navrots-kyi D. O. Method of bio-sensor test system application. Patent UA 129923 U, G01N33/00, G01N33/50, C12Q 1/02. Priority: 22. 03. 2018, u201802896, Issued: 26. 11. 2018, Bull. 22, 7 p. (In Ukrainian). http://base.uipv.org/searchINV/search.ph p?action=viewdetails&IdClaim=252946IdC laim=252946

174. Klyuchko O. M., Biletsky A. Ya., Navrots-kyi D. O. Method of application of biotechnical monitoring system with biosensor (bio-sensor test system). Patent UA 132245 U; G01N33/50. Priority: 22. 03. 2018, u201802893, Issued: 25.02.2019, Bull. 4, 7 p. (In Ukrainian). http://base. uipv.org/searchINV/search.php?action=vi ewdetails&IdClaim=255843

175. Klyuchko O. M., Biletsky A. Ya., Navrots-kyi D. O. Method of application of biotechnical monitoring system with biosensor and sub-system for optical registration. Patent UA 129922 U, G01N33/50. Priority: 22. 03. 2018, u201802894, Issued: 26. 11. 2018, Bull. 22, 10 p. (In Ukrainian). http://base.uipv.org/

searchINV/search.php?action=viewdetails &IdClaim=252945&chapter=descriptionch apter=description

176. Klyuchko O. M. Method of application of biotechnical monitoring system for bioindicators' accounting with biosensor and sub-system for optical registration. Patent UA 129987 U, G01N33/00, C12Q 1/02, C12N 15/00. Priority: 27. 04. 2018, u201804662, Issued: 26. 11. 2018, Bull. 22, 11 p. (In Ukrainian). https://base.uipv.org/ searchINV/search.php?action=viewdetails &IdClaim=253010"IdClaim=253010

177. Klyuchko O. M. Method for cells' dissociation. Patent UA 130672 U, G01N 33/00, C12Q 1/02, C12N 15/00. Priority: 27. 04. 18, u201804668, Issued: 26. 12. 2018, Bull. 24, 7 p. (In Ukrainian). https:// base.uipv.org/searchINV/search.php?actio n=viewdetails&IdClaim=253856&chapter= description"chapter=description

178. Klyuchko O. M., Biletsky A. Ya., Navrotskyi D. Method of application of biotechnical monitoring system with expert subsystem and biosensor. Patent UA 131863 U; G01N33/00, C12Q 1/02, C12N 15/00. Priority: 27. 04. 18, u201804663, Issued: 11. 02. 2019, Bull. 3, 7 p. (In Ukrainian). https://base.uipv.org/searchINV/search. php?action=viewdetails&IdClaim=255364

179. Klyuchko O. M. Method of qualitative analysis of chemical substances. Patent UA 131016 U, G01N33/50, G01N21/78, C12Q 1/60. Priority: 11. 05. 2018, u201805174, Issued: 10. 01. 2019, Bull. 1. (In Ukrainian). https://base.uipv.org/searchINV/search. php?action=viewdetails&IdClaim=254309 "IdClaim=254309

180. Klyuchko O. M., Biletsky A. Ya., Navrots-kyi D. A. Method of quantitative analysis of chemical substances. Patent UA 131524 U; G01N33/50, G01N21/78, C12Q 1/60. Priority: 11. 05. 2018, u201805175, Issued: 25. 01. 2019, Bull. 2, 10 p. (In Ukrainian). http://base.uipv.org/searchINV/search.ph p?action=viewdetails&IdClaim=254915

181. Klyuchko O. M., Biletsky A. Ya. Method of qualitative analysis of hydrocarbons with a high and toxic effect on bioobjects. Patent UA 133676 U; G01N 33/50, G01N 21/78. Priority: 06. 06. 2018, u201806342, Issued: 25. 04. 2019, Bull. 8, 10 p. (In Ukrainian). http://base.uipv.org/searchINV/search.ph p?action=viewdetails&IdClaim=257663&c hapter=biblio

182. Klyuchko O. M., Biletsky A. Ya. Method of qualitative analysis of chemical substances for the influence on electrical currents in bioobjects. Patent UA134142 U; G01N

33/50, G01N 21/78, C12Q 1/60. Priority: 06. 06. 2019, u201806345, Issued: 10. 05. 2019, Bull. 9, 10 p. (In Ukrainian). http://base.uipv.org/searchINV/search. php?action=viewsearchres&page=4

183. Klyuchko O. M., Biletsky A. Ya., Navrots-kyi D. O. Method of monitoring of chemical substances' infuences on organisms in few time intervals. Patent UA; G01N33/00, C12Q 1/02. Priority: 14. 12. 2018, u201812443. (In Ukrainian).

184. Klyuchko O. M., Biletsky A. Ya., Shutko V. N. Method for constructing and testing of physical molecular memory in anisotropic media with molecules — derivatives of phenol. H01B1/121, G01N33/00, C12Q 1/02. Patent UA. Priority: 14. 12. 2018, u201812430. (InUkrainian).

185. Klyuchko O. M., Biletsky A Ya., Lizunov G. V., Shutko V. N. Method of electrical signals generating by bio-elements in the technical hybrid system. A61N1/326, G01N33/00, C12Q 1/02. Patent UA. Priority: 14. 12.

2018, u201812442. (In Ukrainian).

186. Klyuchko O. M., Biletsky A Ya., Lizunov G. V., Shutko V. N. Method for application of the system of large-scale monitoring of bioobjects with possibility of their radar control. G01N33/00, G01S13/88, C12N 15/00. Patent UA. Priority: 14. 12. 2018, u201812444. (In Ukrainian).

187. Klyuchko O. M., Biletsky A. Ya., Lizunov G. V., Piankova O. V. Method of application of biosensor test-system with databases. G01N33/00, C12Q 1/02. Patent UA. Priority: 17. 01. 2019, u201900476. (In Ukrainian).

188. Klyuchko O. M., Biletsky A. Ya., Lizunov G. V., Piankova O. V. Method of application of monitoring system with biosensor and databases. G01N33/00, C12Q 1/02, G06F11/20. Patent UA. Priority: 17. 01.

2019, u201900476. (In Ukrainian).

189. Klyuchko E. M., Tzyndrenko A. Ya. The method for dissociation of hippocampal cells. C12Q 1/02. Priority: 31. 01. 1986; Issued: 30. 01. 1988, Bull. N 4, 3 p. (In Russian). http:// www.findpatent.ru/patent/137/1370136.html

190. Microsoft Academy: Methods and means of software engineering. https://www. intuit.ru/studies/courses/2190/237/ lecture/6124.

191. Chomonenko A. D., Tzygankov V. M., Maltzev M. G. Databases. 5-th edition. Moskva: Binom Press. 2006, 736 p. (In Russian).

192. Harrington Jan L. Object-oriented database design clearly explained. USA: Academic Press. 2005, 270 p.

БЮЛОГШН1 БАЗИ ДАНИХ: ЗАСТОСУВАННЯ ОБ'СКТНО-ОР1€НТОВАНОГО СИСТЕМНОГО АНАЛ1ЗУ

О. М. Ключко1, Л. П. Бучацький2, О. В. Мележик3

Институт експериментально1 патологи, онкологи i радмбмлоги iM. М.В. Кавецького

НАН Укра1ни, Ки1в 2Ки1вський нащональний унiверситет iMeHi Тараса Шевченка, Укра1на 3Вiдкритий мiжнародний унiверситет розвитку людини «Укра1на», Ки1в

E-mail: kelenaXX@ukr.net

Метою роботи було розробити електронну базу даних бшлоичних органiзмiв (риб, BipyciB) на основi методiв, якi дотепер застосовували у математищ, технiцi, фiзицi. Пiд час виконання роботи використовували методи об'ектного системного аналiзу, побудови моделей предметних областей (просмр задач, простiр рiшень), ER-дiаграми, методи конструювання баз даних та шшь У статтi розглянуто приклади-прототипи сучасних баз даних з шформащею про риб, що 1х розроблено протягом останшх рокiв у кра1нах Захщно! ввропи та Америки. Наведено огляд застосованих методiв, концепцiй та термшологи з областей об'ектного системного аналiзу, конструювання баз даних. У висновках подано опис сконструйовано! бази даних та практичш рекомендаций з розроблення баз даних з шформащею щодо в^чизняних бiологiчних органiзмiв для електронних шформацшних систем.

Ключовi слова: об'ектно-орiентований систем-ний аналiз, бiологiчнi об'екти, електронш iнформацiйнi системи, риби, вiруси.

БИОЛОГИЧЕСКИЕ БАЗЫ ДАННЫХ :

ИСПОЛЬЗОВАНИЕ ОБЪЕКТНО-ОРИЕНТИРОВАННОГО СИСТЕМНОГО АНАЛИЗА

Е. М. Ключко1, Л. П. Бучацкий2, О. В. Мележик3

1Институт экспериментальной патологии,

онкологии и радиобиологии им. М. В. Кавецкого НАН Украины, Киев 2Киевский национальный университет имени Тараса Шевченко, Украина 3Открытый международный университет развития человека «Украина», Киев

E-mail: kelenaXX@ukr.net

Целью работы было разработать электронную базу данных биологических организмов (рыб, вирусов) на основе методов, которые до сих пор применялись в математике, технике, физике. При выполнении работы были применены методы объектного системного анализа, построения моделей предметных областей (пространство задач, пространство решений), ER-диаграммы, методы конструирования баз данных и другие. В статье рассмотрены примеры-прототипы современных баз данных с информацией о рыбах, разработанные в последние годы в странах Западной Европы и Америки. Приведен обзор применяемых методов, концепций и терминологии из областей объектного системного анализа, конструирования баз данных. В выводах приведено описание сконструированной базы данных и практические рекомендации по разработке баз данных с информацией об отечественных биологических организмах для электронных информационных систем.

Ключевые слова: объектно-ориентированный системный анализ, биологические объекты, электронные информационные системы, рыбы, вирусы.