KR101741952B1 - Method of visualizing a massive reaction mechanism using semantic model - Google Patents

Abstract

A method for visualizing a large scale MCMT reaction using a semantic model is disclosed. The visualization method according to an exemplary embodiment includes a step of generating a hierarchical structure semantic model including a reaction mechanism hierarchical structure and a human body semantic structure based on multi-component, multi-targets (MCMT) reaction mechanism data, And visualizing the reaction mechanism hierarchical structure and the human body semantic structure in association with each other.

Description

[0001] METHOD OF VISUALIZING A MASSIVE REACTION MECHANISM USING SEMANTIC MODEL [0002]

The following embodiments relate to a method of visualizing a large scale MCMT response using a semantic model.

3D human visualization is mostly focused on aesthetic or realistic description of the human body, and visualizes raw data in one dimension without systematization corresponding to human body structure.

In order to analyze the three - dimensional human model, researches for analyzing the reaction mechanism inside the human body in molecular basis have been actively conducted. Such data is very large and difficult to analyze and visualize in real time.

Large quantities of data are generated in the visualization of molecular reactions. Millions to tens of millions of data are analyzed and modified. Conventional visualization methods are not suitable for analysis because they are vulnerable to information transformation and provide vast amount of information without filtering. For efficient analysis, intensive information should be provided systematically, and a structure optimized for the human body is needed in the movement between information.

Embodiments can provide a hierarchical data transfer structure for efficiently processing large amounts of data and a real-time visualization technique utilizing a two-level hierarchical structure for real-time visualization of a large scale reaction.

The MCMT of the present invention refers to a complex reaction mechanism targeting a plurality of components and a plurality of targets, and a reaction represented by a plurality of paths in a complex network must be analyzed and visualized in a complex manner.

A method of visualizing a large-scale MCMT reaction according to an exemplary embodiment includes generating a hierarchical semantic model including a reaction mechanism hierarchical structure and a human semantic structure based on MCMT (multi-component, multi-targets) reaction mechanism data And visualizing the response mechanism hierarchy structure and the human body semantic structure in association with each other based on a vertical relationship between the nodes.

The generating may include visualizing the MCMT reaction mechanism data into the reaction mechanism hierarchical structure and visualizing the MCMT reaction mechanism data into the human body semantic structure.

The MCMT reaction mechanism data may include a plurality of nodes and relations between the plurality of nodes, and each of the plurality of nodes may be at least one of a molecule, a cell, an organ, and a human body .

Wherein the step of visualizing the hierarchical structure comprises: dividing the MCMT reaction mechanism data into a plurality of nodes; defining a relationship between horizontal nodes of the plurality of nodes; And defining a relationship.

Wherein visualizing in the human semantic structure comprises: dividing the MCMT reaction mechanism data into visualization regions between a plurality of nodes; matching positions of the plurality of nodes based on a three-dimensional physical location; And defining a relationship between the vertical nodes of the nodes.

The step of generating the semantic model of the hierarchical structure including the reaction mechanism hierarchy structure and the human body semantic structure based on the multi-component, multi-targets (MCMT) reaction mechanism data may be performed in real time based on the web.

The method may further comprise labeling and indexing the multi-component, multi-targets (MCMT) response mechanism data based on attributes of the plurality of nodes, attributes of the relationships, and semantic information.

The attributes of the plurality of nodes include at least one of a gene, a metabolite, a molecular complex, a gene ontology term (GO Term), and a phenotype, which are attributes of a molecule And the attributes of the relationships are selected from the group consisting of Activate, Regulate, Inhibit, React, Express, Repress, Trans-activate, Act, , And translocate (Translocate).

The semantic information may include node index information having importance and position values of the plurality of nodes and directions of the relations.

The method includes the steps of setting an attribute of the first node and a relation of the first node at a first one of the plurality of nodes to a key of the first node, And searching for one node.

1 is a schematic block diagram of a system for performing a method of visualization of large MCMT reactions.
Fig. 2 shows an example of a flowchart for explaining an operation method of the controller shown in Fig.
Fig. 3 shows an example of a flowchart for generating the semantic model shown in Fig.
FIG. 4 shows an example of a flowchart for illustrating a method of visualizing the hierarchical structure of reaction mechanisms shown in FIG.
FIG. 5 shows an example of a flowchart for illustrating a method of visualizing the human body semantic structure shown in FIG.
FIG. 6 shows an example of a screen displayed on the display in the search process of the system shown in FIG. 1;
FIG. 7 shows an example of a process in which the system shown in FIG. 1 searches for a node using a key at a current location.
Figure 8 shows an example of a reaction mechanism hierarchy visualization process.
Figure 9 shows an example of a human body semantic structure visualization process.

It is to be understood that the specific structural or functional descriptions of embodiments of the present invention disclosed herein are presented for the purpose of describing embodiments only in accordance with the concepts of the present invention, May be embodied in various forms and are not limited to the embodiments described herein.

Embodiments in accordance with the concepts of the present invention are capable of various modifications and may take various forms, so that the embodiments are illustrated in the drawings and described in detail herein. It should be understood, however, that it is not intended to limit the embodiments according to the concepts of the present invention to the particular forms disclosed, but includes all modifications, equivalents, or alternatives falling within the spirit and scope of the invention.

The terms first, second, or the like may be used to describe various elements, but the elements should not be limited by the terms. The terms may be named for the purpose of distinguishing one element from another, for example without departing from the scope of the right according to the concept of the present invention, the first element being referred to as the second element, Similarly, the second component may also be referred to as the first component.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between. Expressions that describe the relationship between components, such as "between" and "between" or "neighboring to" and "directly adjacent to" should be interpreted as well.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises ", or" having ", and the like, specify that there is an indication of a feature, a number, a step, an operation, an element, a part or a combination thereof, , Steps, operations, components, parts, or combinations thereof, as a matter of principle.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the meaning of the context in the relevant art and, unless explicitly defined herein, are to be interpreted as ideal or overly formal Do not.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these embodiments. Like reference symbols in the drawings denote like elements.

The present invention proposes a semantic model that combines two hierarchical modeling for analysis of a three-dimensional human body model. The mechanism of the reaction can be divided into visibility data, which should have high clarity, and semantic data, where the anatomical meaning is important. For the visual data, it is necessary to use a quad-tree structure because it should be easy to move between layers. In case of semantic data, it is easy to move in the same layer. Therefore, a tree structure reflecting anatomical information can be used. Visualization through this mixed model can be more efficient than visualization with each method alone. The present invention can be validated through implementation and applied to modeling for human body analysis.

The present invention proposes a hierarchical semantic model that reflects an anatomical meaning of the human body and is easy to search. Hierarchical models can be applied to effectively visualize over tens of millions of data that can not be visualized at one time. For this purpose, the hierarchical semantic model reflects the human body component, and the object data can be composed of a set of information of the body, organ, cell, and molecule. At this time, the body, organs, cells, and molecules can constitute one node, and the nodes of the body, organs, and cells can be classified into the upper nodes, and the nodes of the molecules can be defined as the lower nodes. There may be a distinction between the superordinate concept and the subordinate concept even within an upper node. Body nodes are superior to organ and cell nodes, and cell nodes can be subordinate to body and organ nodes. That is, it can be determined according to the relative position between the nodes. For example, a cell node is an ancestor rather than a molecular node, but may be a lower node than a long-term node. Top nodes with search can be modeled as a Quad-Tree structure for convenience of navigation, and sub-nodes where biology and anatomical significance are important can be modeled as a tree structure based on reaction mechanism information. In Quad-Tree, it is possible to increase the efficiency by enabling the partial search including information on the degree of human body process (molecule) unit by node unit. The visualization method can visualize the subordinate node for 2D for clarity and 3D for the parent node to represent the 3D relationship.

In the present invention, a Multi-Component Multi-Target (MCMT) reaction mechanism file is used as raw data. MCMT refers to a complex reaction mechanism for a large number of components and a large number of targets. It is necessary to analyze and visualize the reactions represented by multiple paths in a complex network. The MCMT Reaction Mechanism File is a JSON file obtained using the CODA database through biological reaction analysis. The MCMT reaction mechanism used is 1,610,632 biological processes (molecules) and 14,037,430 relationships As shown in Fig. That is, the MCMT reaction mechanism data includes a plurality of nodes (N: = {n ₁ , ... n _n }) having a structure of a molecule (Molecule) - a cell - an organ - (R: = {r ₁ , ... r _m }) between the plurality of nodes. At this time, the relationships between the plurality of nodes and the plurality of nodes may include attribute information, and the i-th node and the i-th relation may be calculated according to the following equation.

The i-th node may have the attributes of the node (A (n _i )), the importance of the node (V _{n i} ), and the location of the node (P (n _i )). The attributes of the node (A (n _i ): = {An ₁ , ... An ₅ }) are the properties of the molecule, the gene, the metabolite, the molecular complex, A term (GO Term), and a phenotype. V (n), which means importance, can be an attribute defined based on the frequency and intensity of the response. P (n) is a point value having coordinates of a three-dimensional space, and can be the basis of a search at a lower node, i.e., a molecular node.

The i-th relationship may have two node index information S (ri), E (ri) having the relationship attribute A (r _i ) and direction.

The attributes (A (r _i ): = {Ar ₁ , ... Ar ₉ }) of the relationships between the plurality of nodes are the attributes of the intermolecular relationship: Activate, Regulate, Inhibit, And may include at least one of React, Express, Repress, Trans-activate, Act, and Translocate.

FIG. 1 is a schematic block diagram of a system for performing a method of visualizing a large scale MCMT reaction, FIG. 2 is an example of a flowchart for explaining an operation method of the controller shown in FIG. 1, And generates the semantic model.

FIG. 6 shows an example of a screen displayed on the display in the search process of the system shown in FIG. 1, FIG. 7 shows an example of a process in which the system shown in FIG. For example.

Referring to Figures 1-3, a system 10 for performing a method of visualizing a large scale MCMT reaction may include a controller 100 and a display 200.

The controller 100 may be implemented with one or more processors including at least one core.

The controller 100 may receive the MCMT reaction mechanism data (DATA 1). The controller 100 may generate a hierarchical semantic model based on the MCMT reaction mechanism data (DATA 1) (S210). A hierarchical semantic model can include a hierarchy of reaction mechanisms and a human semantic structure.

In addition, the controller 100 can determine the degree of the number of molecules to be displayed on the screen, and can visualize molecules with high importance first. This can shorten the search and search time of the user of the system 10 performing the method of visualization of the large MCMT response.

Specifically, the controller 100 can visualize the MCMT reaction mechanism data (DATA 1) as a reaction mechanism hierarchical structure (S310). The controller 100 can visualize the MCMT reaction mechanism data (DATA 1) in a human body semantic structure (S320). For example, the controller 100 may perform the visualization in the hierarchical structure of the response mechanism and the visualization in the human semantic structure simultaneously or sequentially. At this time, the operation of visualizing the hierarchical structure of the reaction mechanism may precede the operation of visualizing the human body semantic structure, and the operation of visualizing the human body semantic structure may precede the operation of visualizing the hierarchical structure of the reaction mechanism. In addition, the controller 100 can perform visualization in the hierarchical structure of the reaction mechanism and visualization in the human body semantic structure in real time based on the web. In other words, visualization with the reaction mechanism hierarchical structure and visualization with the human body semantic structure can be performed (or progressed) in real time based on the web.

The controller 100 may label and index the MCMT response mechanism data (DATA 1) based on attributes of nodes, attributes of relationships, and semantic information. The semantic information may include the importance and position of the node. The controller 100 may store the representative semantic information summarized in the hierarchical structure, and may efficiently filter unnecessary information using the summarized representative semantic information. For example, the controller 100 may include a memory (not shown) for storing representative semantic information, and the memory may be implemented internally and / or externally of the controller 100.

In addition, the controller 100 may set the attribute of the first node and the relationship between the first nodes as a key of the first node. Similarly, the controller 100 may set the respective keys to the remaining nodes. Thus, the controller 100 can search for and search for a specific node using the key of the node.

The controller 100 may associate the reaction mechanism hierarchy structure and the human body semantic structure (S220). That is, the controller 100 can visualize the reaction mechanism hierarchical structure and the human body semantic structure based on the MCMT reaction mechanism data (DATA 1), and can generate the semantic visualization data (DATA 2) by linking the two structures. The controller 100 may transmit the semantic visualization data (DATA 2) to the display 200.

The display 200 may display the semantic visualization data (DATA 2) transmitted from the controller 100 _. A user of the system 10 performing a method of visualization of a large MCMT response can facilitate the search and navigate of the mechanistic response by using the semantic visualization data (DATA 2) displayed on the display 200 have. The screen displayed on the display 200 by the search process performed by the system 10 is as shown in FIG. 6, and the process of searching and searching using the keys of the nodes at the current location is as shown in FIG. 7 .

FIG. 4 is an example of a flowchart for explaining a method of visualizing the hierarchical structure of the reaction mechanism shown in FIG. 3, and FIG. 8 shows an example of a process of visualizing the hierarchical structure of the reaction mechanism.

Referring to FIGS. 4 and 8, the operation of the controller 100 to visualize the response mechanism hierarchical structure may be performed in the following order.

The controller 100 may divide the MCMT response mechanism data (DATA 1) into a plurality of nodes of a lower concept (S410).

For example, the controller 100 may divide the human node of the MCMT response mechanism data (DATA 1) into at least one of an organ node, a cell node, and a molecular node. In the same way, the controller 100 may divide the organ node of the MCMT reaction mechanism data (DATA 1) into at least one of a cell node and a molecular node, and divides the cell node of the MCMT reaction mechanism data (DATA 1) can do. At this time, when the sub-concept no longer exists, the controller 100 can stop the division.

The controller 100 may define a relationship between horizontal nodes of a plurality of nodes (S420). Horizontal nodes can be, for example, organ nodes and organ nodes, cell nodes and cell nodes, or molecular and molecular nodes that do not have an upper or lower concept between nodes.

The definition of the relationship between horizontal nodes is continuously defined from the definition of the relation of the lowest node to the cell node unit, and it can facilitate the horizontal movement in the quad-tree. Since the maximum and minimum importance information of each branch of the node is analyzed and used for visualization, the amount of information required for the horizontal search can be reduced. The controller 100 may define a relationship between horizontal nodes and may link related horizontal nodes.

The controller 100 may define a relationship between vertical nodes of a plurality of nodes (S430). Vertical nodes may refer to nodes that follow the level of a parent or child concept, for example, a molecule-cell-organ-organism.

After the controller 100 defines the relationships between the vertical nodes of the plurality of nodes, the quad-tree can be completed using the set concept. In this case, each molecular node included in the mechanism reaction can be defined as a vertical layer of the quad-tree based on the importance and position, and the relationship between the nodes in the quad-tree is defined do. Referring to FIG. 8, an example of a result obtained by visualizing the MCMT reaction mechanism data (DATA 1) as a reaction mechanism hierarchical structure can be referred to.

FIG. 5 shows an example of a flowchart for explaining a method of visualizing the human body semantic structure shown in FIG. 3, and FIG. 9 shows an example of a human body semantic structure visualization process.

5 and 9, the operation of visualizing the controller 100 in a human body semantic structure can be performed in the following order.

The controller 100 may divide the MCMT reaction mechanism data (DATA 1) into visualization areas (S510). For example, the controller 100 may visually divide a node and a node so that a user of the system 10 performing a method of visualization of a large MCMT response can distinguish different nodes.

The controller 100 may match the positions of the plurality of nodes based on the three-dimensional actual position of the MCMT reaction mechanism data (DATA 1) divided into the visualization areas (S520). Since the ancestor nodes are conceptual structures that can be perceived by humans, they can be expressed in 3D visualization with higher reality. It is possible to expect the effect of reducing the search process by distinguishing the upper node and lower node based on the cell node according to the characteristics in the human body model and applying a suitable model according to each characteristic.

The controller 100 may define a relationship between vertical nodes of a plurality of nodes (S530). Vertical nodes may refer to nodes that follow the level of a parent or child concept, for example, a molecule-cell-organ-organism.

After the controller 100 defines the relationships between the vertical nodes of the plurality of nodes, the quad-tree can be completed using the set concept. Referring to FIG. 9, an example of a result obtained by visualizing DATA MCMT reaction mechanism data (DATA 1) in a human body semantic structure can be referred to.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA) , A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (10)

Generating a semantic model of a hierarchical structure including a response mechanism hierarchical structure and a human semantic structure based on MCMT (multi-component, multi-targets) reaction mechanism data including information on a plurality of nodes ; And
Visualizing the response mechanism hierarchical structure and the human body semantic structure in association with each other based on a vertical relationship between the plurality of nodes
Lt; / RTI >
Wherein each of the plurality of nodes is one of a human body, an organ, a cell, and a molecule,
Wherein the vertical relationship is a relationship in which the human body is the top node and the parent node is included in the order of the organ, the cell,
A method for visualizing large MCMT reactions.
The method according to claim 1,
Wherein the generating comprises:
Visualizing the MCMT reaction mechanism data into the reaction mechanism hierarchical structure; And
Visualizing the MCMT reaction mechanism data in the human body semantic structure
≪ / RTI >
The method according to claim 1,
Wherein the information about the plurality of nodes includes relations between the plurality of nodes and the plurality of nodes
A method for visualizing large MCMT reactions.
3. The method of claim 2,
The step of visualizing the reaction mechanism hierarchy comprises:
Dividing the MCMT reaction mechanism data into a plurality of nodes;
Defining a relationship between horizontal nodes of the plurality of nodes; And
Defining a relationship between the vertical nodes of the plurality of nodes
Lt; / RTI >
The reaction mechanism hierarchical structure is a structure in which a mechanism of reaction between nodes at the same level with respect to the organ, the cell, or the molecule is expressed by a network structure
A method for visualizing large MCMT reactions.
3. The method of claim 2,
Wherein visualizing the human body semantic structure comprises:
Dividing the MCMT reaction mechanism data into visualization regions between a plurality of nodes;
Matching the positions of the plurality of nodes based on the three-dimensional actual position; And
Defining a relationship between the vertical nodes of the plurality of nodes
≪ / RTI >
The method according to claim 1,
Wherein the generating comprises:
Based on the web
A method for visualizing large MCMT reactions.
The method of claim 3,
Labeling and indexing the multi-component, multi-targets (MCMT) reaction mechanism data based on attributes of the plurality of nodes, attributes of the relationships, and semantic information
The method comprising the steps of:
8. The method of claim 7,
The attributes of the plurality of nodes include at least one of a gene, a metabolite, a molecular complex, a gene ontology term (GO Term), and a phenotype, which are attributes of a molecule and,
The attributes of the relationships may be selected from the group consisting of Activate, Regulate, Inhibit, React, Express, Repress, Trans-activate, Act, RTI ID = 0.0 > (Translocate) < / RTI >
A method for visualizing large MCMT reactions.
8. The method of claim 7,
The semantic information may include:
An importance and position value of the plurality of nodes; And
Node index information < RTI ID = 0.0 >
≪ / RTI >
8. The method of claim 7,
Setting an attribute of the first node and a relation of the first node at a first one of the plurality of nodes to a key of the first node; And
Searching for the first node using the key
The method comprising the steps of:

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