Pathway analysis device, pathway analysis method, and pathway analysis program

Abstract

The system is equipped with a partial common pathway detection unit (12) that detects common parts from the start point to the end point of a plurality of disease pathways, which are represented as pathway diagrams of intermolecular interactions between molecules related to a disease for each of a plurality of diseases related to a state change, and a common difference information providing unit (13) that provides information on the detected partial common pathways or molecules contained in the partial common pathways, and non-partial common pathways other than the partial common pathways or molecules contained in the non-partial common pathways, in a state in which it is possible to identify whether they are related to a partial common pathway or a non-partial common pathway.The system provides information to show which parts are common and which parts are not common from the start point to the end point of the pathway in relation to the disease pathway related to the disease before the state change and the disease pathway related to the disease after the state change, thereby making it possible to understand changes in the pathway associated with a change in the state of the disease.

Description

[Technical field]

[0001] The present invention relates to a pathway analysis device, a pathway analysis method, and a pathway analysis program, and is particularly suitable for use in a technique for analyzing pathways in which intermolecular interactions are represented as a route diagram. [Background technology]

[0002] Conventionally, pathways (also called molecular networks) that show molecular interactions as a route diagram have been known. Pathways are expressed by showing molecules such as genes and proteins as symbols such as circles and squares, and connecting the symbols with arrows that indicate the interaction between molecules. By visualizing molecular interactions in this way, it becomes easier to understand life phenomena, for example by investigating which pathways contain genes whose expression levels have changed. Pathways are widely used, for example, in the fields of disease treatment and drug discovery.

[0003] There is known a technology that generates a molecule-function network of a required range by search, and makes it possible to estimate a bio-event in which an arbitrary biomolecule is directly or indirectly involved in the expression (see, for example, Patent Document 1). Patent Document 1 discloses a technology that finds a portion common to a plurality of molecule-function networks, and estimates that a biomolecule that appears in the molecule-function network of the common portion is highly likely to play an important role in the expression of a bio-event.

[0004] By the way, although a disease may be considered to be a fixed state, in reality, there are various changes in the state of the disease. Changes in the state of the disease refer to, for example, the development of hepatocellular carcinoma from cirrhosis (nonalcoholic fatty liver disease (NAFLD) → nonalcoholic steatohepatitis (NASH) → hepatocellular carcinoma (HCC)), and the occurrence of complications such as neuropathy, retinopathy, nephropathy, and arteriosclerosis caused by diabetes.

[0005] In addition, even in the same disease, there are various changes in the state of symptoms. In other words, symptoms may be used to confirm the diagnosis of a disease or as a treatment target, but the severity and continuity of the symptoms may vary even in the same disease. For example, in diabetic nephropathy, the state of symptoms may change from microalbuminuria, which indicates mild protein leakage, to albuminuria, and then to severe, persistent proteinuria.

[0006] Conventionally, it has been difficult to capture such changes in disease or symptom status as changes in intermolecular networks, and the scope of drug discovery target search using intermolecular networks has been limited. The above-mentioned Patent Document 1 discloses that a molecule-function network of a required scope can be generated using various search functions, but there is no search function that focuses on capturing changes in disease or symptom status as changes in intermolecular networks.

[0007] [Patent Document 1] WO2003 / 077159 publication Summary of the Invention [Problem to be solved by the invention]

[0008] The present invention has been made to solve such problems, and aims to enable changes in the state of a disease or symptom to be understood as changes in a pathway (an intermolecular network). [Means for solving the problem]

[0009] In order to solve the above-mentioned problems, the present invention detects common parts only in a portion of the pathway from the start point to the end point between a plurality of disease pathways that represent, as a pathway diagram, the intermolecular interactions of molecules related to a disease for a plurality of diseases related to a state change, or between a plurality of symptom pathways that represent, as a pathway diagram, the intermolecular interactions of molecules related to a symptom for a plurality of symptoms related to a state change, and provides information in a state in which the detected partial common pathways and non-partial common pathways other than the partial common pathways can be distinguished from each other. Effect of the Invention

[0010] According to the present invention configured as described above, information is provided to show which parts of the disease pathway from the start point to the end point are common and which parts are not common in the relationship between the disease pathway related to the disease before the state change and the disease pathway related to the disease after the state change. Since the state change of the disease appears as a partial change in the disease pathway from the start point to the end point of the pathway, by clearly showing the common and non-common parts of the pathway before and after the state change of the disease, it is possible to understand the change in the pathway accompanying the state change of the disease.

[0011] Furthermore, according to the present invention, information is provided to show which parts from the start point to the end point of the pathway are common and which parts are not common in the relationship between the symptom pathway related to the symptom before the state change and the symptom pathway related to the symptom after the state change. Since the state change of the symptom appears as a partial change in the symptom pathway from the start point to the end point of the pathway, by clearly showing the common and non-common parts in the pathway before and after the state change of the symptom, it is possible to understand the change in the pathway accompanying the state change of the symptom. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram showing an example of a functional configuration of a pathway analysis device according to a first embodiment. [Diagram 2] FIG. 2 is a block diagram showing an example of a functional configuration of a pathway generation device. [Diagram 3] 1 is a block diagram showing an example of a functional configuration of a feature vector calculation device; [Figure 4] FIG. 13 is a diagram showing examples of a disease feature vector and a molecular feature vector. [Diagram 5] 13 is a diagram showing an example in which a partial common path is detected in an upstream section by a partial common path detection unit; FIG. [Figure 6] 13 is a diagram illustrating an example of information provided by a common difference information providing unit. FIG. [Figure 7]FIG. 4 is a block diagram showing another example of the functional configuration of the pathway analysis device according to the first embodiment. [Figure 8] FIG. 11 is a block diagram showing an example of a functional configuration of a pathway analysis device according to a second embodiment. [Figure 9] FIG. 13 is a diagram showing an example of a common pathway detected by a disease symptom common pathway detection unit. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] (First embodiment) Hereinafter, a first embodiment of the present invention will be described with reference to the drawings. Fig. 1 is a block diagram showing an example of a functional configuration of a pathway analysis device 10 according to the first embodiment. As shown in Fig. 1, the pathway analysis device 10 according to the first embodiment includes, as functional configurations, a disease pathway input unit 11, a partial common pathway detection unit 12, and a common difference information provision unit 13.

[0014] The functional blocks 11 to 13 can be configured by any of hardware, DSP (Digital Signal Processor), and software. For example, the functional blocks 11 to 13 are realized by the operation of a program stored in a storage medium such as a RAM, ROM, hard disk, or semiconductor memory under the control of a microcomputer configured with a CPU, RAM, ROM, etc. Note that these functional blocks 11 to 13 indicate the processing procedure of a pathway analysis method.

[0015] The disease pathway input unit 11 inputs a plurality of pathways (hereinafter referred to as disease pathways) generated for a plurality of diseases related to a state change. A plurality of diseases related to a state change is a combination of a plurality of diseases that may progress from one disease to another, such as a combination of NAFLD, NASH, and HCC, or a combination of diabetes, neuropathy, retinopathy, nephropathy, and arteriosclerosis.

[0016] A disease pathway is an intermolecular network that represents the intermolecular interactions of disease-related molecules as a pathway diagram. That is, a disease pathway is a pathway diagram generated such that, among molecules (genes or proteins) related to a disease, molecules that are causative (hereinafter referred to as causative molecules) that act on the disease are placed upstream of the pathway, molecules that are responsive (hereinafter referred to as responsive molecules) that act on the disease are placed downstream of the pathway, and other molecules (hereinafter referred to as connecting molecules) are placed between the causative and responsive molecules. Causality is a property that is believed to have the potential to cause a disease due to the presence or mutation of the molecule. Responsiveness is a property that is believed to have the potential to cause a mutation (change) of a molecule due to the onset of a disease.

[0017] Here, when generating a disease pathway for a specific disease, it is possible to use known information recorded in various literature, databases, etc. as information on a plurality of molecules related to the disease and information on the properties (causality or responsiveness) of the actions of these molecules on the disease. It is also possible to use information obtained by estimating molecules related to a specific disease using a predetermined algorithm and estimating the properties of the actions of the specific molecules on the disease using a predetermined algorithm.

[0018] Any algorithm can be used as an algorithm for predicting related molecules or for predicting molecular properties. For example, a new molecule related to a disease can be predicted by a prediction model trained by machine learning using known information on the association between a disease and a molecule. Also, the properties of a new molecule can be predicted by a prediction model trained by machine learning using known information on the properties of molecules that act on a disease.

[0019] The disease pathway may be created manually using related molecules obtained from known information and / or related molecules estimated by a predetermined algorithm, or may be generated by computer processing. When a disease pathway is generated by computer processing, any algorithm can be used for connecting causative molecules, responsive molecules, and connecting molecules to generate a pathway.

[0020] For example, it is possible to generate pathways by optimization processing using a minimum flow algorithm, using the properties of each molecule and known information showing the connection relationships between molecules. Here, the connection relationships between molecules include, for example, a relationship in which when the expression level of a certain molecule increases (or decreases), the expression level of another molecule increases (or decreases) in conjunction with that.

[0021] The disease pathway generation algorithm can use, for example, the algorithm described in Japanese Patent No. 6915818. In this case, the disease pathway input unit 11 inputs a plurality of disease pathways generated by the algorithm described in Japanese Patent No. 6915818 for each of a plurality of diseases related to a state change.

[0022] The following is a brief explanation of the pathway generation method described in Japanese Patent No. 6915818. Figure 2 is a block diagram showing an example of the functional configuration of the pathway generation device 100.

[0023] The disease characteristic vector identification unit 101 identifies a characteristic vector (hereinafter referred to as a disease characteristic vector) corresponding to the disease name. The disease characteristic vector is data that represents the characteristics of a disease (characteristics that can identify a disease) as a combination of values ​​of multiple elements. As an example, a vector that represents the degree to which a disease name contained as a word in multiple sentences contributes to each sentence is used as the disease characteristic vector.

[0024] While disease names as words tend to be used in sentences that describe diseases, they tend not to be used in sentences that are unrelated to diseases. Furthermore, among sentences that describe diseases, sentences that contain a disease name as a word are sentences that describe that disease, and there is a high possibility that the disease name will not be included in sentences that describe other types of diseases. In other words, sentences that contain disease names as words tend to differ depending on the type of disease that the sentence is about. Therefore, a vector that indicates the degree to which a disease name contributes to which sentence can be used as a feature vector that can identify diseases.

[0025] Such a disease feature vector is calculated, for example, by a feature vector calculation device 200 shown in Fig. 3. The feature vector calculation device 200 shown in Fig. 3 inputs text data related to a text, calculates and outputs a disease feature vector that reflects the relationship between the text and the words contained therein.

[0026] In FIG. 3, the word extraction unit 201 analyzes m sentences (m is any integer equal to or greater than 2) and extracts n words (n is any integer equal to or greater than 2) from the m sentences. For example, a known morphological analysis can be used to analyze the sentences. Note that the m sentences may contain the same word multiple times. In this case, the word extraction unit 201 does not extract the same word multiple times, but extracts only one word. In other words, the n words extracted by the word extraction unit 201 mean n types of words.

[0027] The vector calculation unit 202 calculates m sentence vectors and n word vectors from m sentences and n words. Here, the sentence vector calculation unit 202A calculates m sentences d consisting of q axis components (q is an arbitrary integer of 2 or more) by vectorizing each of the m sentences that are the subject of analysis by the word extraction unit 201 into q dimensions according to a predetermined rule. i→(i=1,2,...,m) (the symbol "→" indicates a vector). In addition, the word vector calculation unit 202B vectorizes each of the n words extracted by the word extraction unit 201 into q dimensions according to a predetermined rule, thereby calculating n word vectors w j → (j = 1, 2, , n) is calculated. i → and the word vector w j The specific calculation method will not be explained here.

[0028] The index value calculation unit 203 calculates the m text vectors d i → and n word vectors w j By taking the inner product of each of them, m sentences d i and n words w j Here, the index value calculation unit 203 calculates an index value that reflects the relationship between m text vectors d i →each q axis component (d 11 ~d mq ) and n word vectors w j →each q axis component (w 11 ~w nq ) as elements of the word matrix W, and calculate the index value matrix DW, each element of which is m×n index values. Here, W t is the transpose of the word matrix.

[0029]

number

[0030] Each element of the index value matrix DW calculated in this way can be said to represent the degree to which a word contributes to a sentence, and the degree to which a sentence contributes to a word. For example, the element dw 12can be said to be a value expressing the degree to which the word w2 contributes to the sentence d1, and also to be a value expressing the degree to which the sentence d1 contributes to the word w2. As a result, each row of the index value matrix DW can be used to evaluate the similarity of sentences, and each column can be used to evaluate the similarity of words.

[0031] The feature vector identification unit 44 identifies, for each of a plurality of disease names among the n words, a word index value group consisting of m index values ​​for one disease name as a disease feature vector. That is, as shown in Fig. 4(a), the feature vector identification unit 44 identifies, as a disease feature vector for each disease name, a word index value group related to a word corresponding to a disease name among n word index value groups (m index values ​​per column) constituting each column of the index value matrix DW.

[0032] Returning to Fig. 2, the configuration of the pathway generation device 100 will be described. The associated molecule inference unit 102 infers a plurality of molecules associated with a disease by inputting the disease feature vector identified by the disease feature vector identification unit 101 to a first trained model stored in advance in a first model storage unit 111. Here, the first trained model has been machine-learned so as to output information on a molecule corresponding to a molecular feature vector similar to a disease feature vector when the disease feature vector is input, based on the similarity between the disease feature vector and the molecular feature vector.

[0033] The molecular feature vector used here is data that expresses the features of molecules such as proteins and genes (features that can identify molecules) as a combination of multiple element values. As an example, a vector that expresses the degree to which a molecule name contained as a word in multiple sentences contributes to each sentence is used as the molecular feature vector. This molecular feature vector can also be calculated by the feature vector calculation device 200 shown in FIG. 3.

[0034] That is, for each of a plurality of molecular names among the n words, the feature vector identification unit 44 identifies a word index group consisting of m index values ​​for one molecular name as a molecular feature vector. Specifically, as shown in Fig. 4(b), the feature vector identification unit 44 identifies a word index group related to a word corresponding to a molecular name among n word index groups (m index values ​​per column) constituting each column of the index value matrix DW as a molecular feature vector for each molecular name.

[0035] A disease feature vector for a plurality of disease names is calculated, and a molecule feature vector for a plurality of molecule names is calculated by the feature vector calculation device 200. Then, machine learning of a first trained model is performed in advance using these data sets, and the first trained model trained based on the similarity between the disease feature vector and the molecule feature vector is stored in the first model storage unit 111.

[0036] Here, the similarity between the disease feature vector and the molecule feature vector can be evaluated in various ways. For example, a method can be applied in which features are extracted from each of the disease feature vector and the molecule feature vector using a predetermined function, and the similarity of the features is evaluated. Alternatively, the Euclidean distance or cosine similarity between the word index value group of the disease feature vector and the word index value group of the molecule feature vector may be used, or the edit distance may be used.

[0037] The molecular property estimation unit 103 inputs the disease feature vector identified by the disease feature vector identification unit 101 and the molecular feature vector identified for multiple molecules estimated by the related molecule estimation unit 102 into a second trained model stored in the second model memory unit 112, and estimates the probability that each of the multiple molecules estimated to be associated with a disease is causative or responsive as a molecular property acting on the disease.

[0038] Here, the second trained model has been machine-trained to output the probability that the molecular property is causative or responsive when a disease feature vector and a molecular feature vector are input, using a disease feature vector, a molecular feature vector, and a dataset of property information representing the properties of molecules that act on a disease as training data.

[0039] The pathway generation unit 104 uses the molecular properties estimated by the molecular property estimation unit 103 and a known knowledge database showing the connection relationships between molecules to generate a pathway (intermolecular network) that represents intermolecular interactions as a route diagram, such that for multiple molecules whose association with a disease is estimated by the related molecule estimation unit 102, causative molecules are located upstream and responsive molecules are located downstream, and the connection relationships shown in the knowledge database are reflected.

[0040] At this time, the pathway generation unit 104 generates the pathway, for example, by utilizing a minimum flow algorithm, so that causative molecules whose probability value estimated by the molecular property estimation unit 103 to be causative is greater than a first threshold Th1 are placed upstream of the pathway, responsive molecules whose probability value is less than a second threshold Th2 (Th1>Th2) are placed downstream of the pathway, and connecting molecules whose probability value is greater than the second threshold Th2 and less than the first threshold Th1 are placed between the causative molecules and the responsive molecules.

[0041] The above is an outline of the pathway generation method described in Japanese Patent No. 6915818.

[0042] Returning to Fig. 1, the partial common pathway detection unit 12 detects common parts between multiple disease pathways input by the disease pathway input unit 11, which are only partially common between the start and end points of the pathway. For example, the partial common pathway detection unit 12 divides the pathway from the start to the end point into an upstream section, a midstream section, and a downstream section, and detects common parts between multiple disease pathways only in any of the divided sections. Such partially common pathways are called partial common pathways.

[0043] As an example, the second or third layer counted downstream from the start point of the route is the upstream section, the second or third layer counted upstream from the end point of the route is the downstream section, and the section between the upstream section and the downstream section is the midstream section. For example, the user can specify in which divided section of the upstream section, midstream section, or downstream section the partial common route is to be detected. Alternatively, the partial common route detection unit 12 may detect the partial common route in each of the upstream section, midstream section, and downstream section.

[0044] FIG. 5 is a diagram showing an example in which a partial common pathway is detected in an upstream section that is two layers downstream from the starting point of the pathway. For the sake of simplicity, only some of the pathways included in the entire pathway (intermolecular network) are shown. In the pathway (part of the whole) shown in FIG. 5, diamond-shaped nodes represent causative molecules, square-shaped nodes represent responsive molecules, and oval-shaped nodes represent connecting molecules. The numbers attached to each node are provisionally written as identifiers for each molecule, and nodes with the same number indicate that they are the same molecule.

[0045] FIG. 5(a) shows a disease pathway for disease A (e.g., NASH) before a state change, and FIG. 5(b) shows a disease pathway for disease B (e.g., HCC) after a state change. A partial pathway 501 in the upstream section shown in FIG. 5(a) and a partial pathway 502 in the upstream section shown in FIG. 5(b) are partial common pathways common to the two disease pathways. That is, two molecules 511 and 512 included in the partial pathway 501 shown in FIG. 5(a) are the same as two molecules 513 and 514 included in the partial pathway 502 shown in FIG. 5(b). In the disease pathway of disease A shown in FIG. 5(a), the part other than the partial common pathway 501 is a non-partial common pathway. In addition, in the disease pathway of disease B shown in FIG. 5(b), the part other than the partial common pathway 502 is a non-partial common pathway.

[0046] In Fig. 5(b), there is a causative molecule 515 in the first layer, and a path is connected from the causative molecule 515 to a connecting molecule 514 in the second layer, but such a path does not exist in Fig. 5(a), and therefore is not detected as a partial common path. Furthermore, when a process for detecting a partial common path is performed by specifying an upstream section, even if a partial common path exists in the midstream section or downstream section, it is not detected by the partial common path detection unit 12.

[0047] The partial common path detection unit 12 may detect a common portion at any part of the path from the starting point to the end point without dividing the path into an upstream section, a midstream section, and a downstream section. However, the common portion to be detected is limited to a predetermined length or less (for example, a length common to two or three layers).

[0048] The common difference information providing unit 13 provides information in a state in which the partial common path detected by the partial common path detection unit 12 and non-partial common paths other than the partial common path can be distinguished from each other (hereinafter referred to as a state in which the common difference can be distinguished).

[0049] For example, the common difference information providing unit 13 can display pathways in a graph format on a display, and display the partial common pathways in a state in which they can be distinguished from the other non-partial common pathways, as shown in Fig. 5. As an example of a distinguishable state, it is possible to surround the partial common pathways 501 and 502 with a rectangular frame as shown in Fig. 5. Alternatively, the partial common pathways 501 and 502 may be highlighted or emphasized, or the partial common pathways 501 and 502 may be displayed in a color different from that of the non-partial common pathways.

[0050] In the example shown in Fig. 5, it is possible to estimate that the causative molecule 215 acts as a stimulus on the second layer connectivity molecule 514 in the disease pathway for disease A before the state change, causing a change in the midstream to downstream pathway, and as a result, disease A may have progressed to disease B. In this case, for example, it is possible to recognize the causative molecule 215 as a candidate drug discovery target molecule.

[0051] As another example of the information providing method, the common difference information providing unit 13 may output the pathway in a spreadsheet format or text format as shown in Fig. 6, so that the partial common pathway can be distinguished from the other non-partial common pathways. As an example of a distinguishable state in this case, as shown in Fig. 6(a), it is possible to highlight the molecular names included in the partial common pathway in the entire pathway from the starting point to the end point or to use a different color. Alternatively or in addition to this, the molecular names included in the partial common pathway and the molecular names included in the non-partial common pathway may be displayed separately as shown in Fig. 6(b).

[0052] As described above, although only pathways including partial common pathways are illustrated in FIG. 5, other pathways not including partial common pathways are also present in the pathways input by the disease pathway input unit 11. The common difference information providing unit 13 may provide information on partial common pathways and non-partial common pathways only for pathways including partial common pathways. In other words, information on other pathways not including partial common pathways may not be provided.

[0053] As described in detail above, in this embodiment, common parts are detected between multiple disease pathways generated for multiple diseases related to status changes, which are only a portion of the pathway from the start point to the end point, and information is provided in a state in which the detected partial common pathways and non-partial common pathways other than the partial common pathways can be distinguished from each other.

[0054] According to the present embodiment configured as described above, information is provided to show which parts from the start point to the end point of the pathway are common and which parts are not common in the relationship between the disease pathway related to the disease before the state change and the disease pathway related to the disease after the state change. Since the state change of the disease appears as a partial change in the pathway from the start point to the end point of the pathway, by clearly showing the common and non-common parts in the pathway before and after the state change of the disease, it is possible to understand the change in the pathway accompanying the state change of the disease.

[0055] In addition, by detecting partial common pathways by specifying the upstream, midstream, and downstream sections, it is possible to perform analysis that focuses on the information you want. For example, when detecting partial common pathways by specifying the upstream section, it is possible to select target molecule candidates that may cause changes in responsiveness (disease phenotype) in diseases related to two state changes. In addition, when detecting partial common pathways by specifying the downstream section, even if the responsiveness appears to be the same in diseases related to two state changes, it is possible to extract differences in causes (stimuli) and analyze the causes of state changes due to the disease. In addition, by detecting partial common pathways by specifying the midstream section, it is possible to analyze factors related to differences in both causality and responsiveness.

[0056] In the above embodiment, an example has been described in which the feature vectors calculated by the feature vector calculation device 200 shown in Fig. 3 are used as the disease feature vector and the molecule feature vector, but the present invention is not limited to this. For example, as long as the vector represents the degree to which a disease name or molecule name contained as a word in a plurality of sentences contributes to each sentence, the vector is not limited to the feature vector calculated by the feature vector calculation device 200 shown in Fig. 3. In addition, it is not essential that the feature vector be calculated from the relationship between the sentence and the word, and as long as the feature vector can identify the features of a disease or molecule, it can be used as the disease feature vector and the molecule feature vector.

[0057] In the above first embodiment, an example of detecting a partial common pathway was described in which a disease pathway, which represents the intermolecular interactions of molecules related to a disease as a pathway diagram, is the target. However, a partial common pathway may also be detected in a pathway, which represents the intermolecular interactions of molecules related to a symptom as a pathway diagram (hereinafter referred to as a symptom pathway).

[0058] Figure 7 is a block diagram showing an example of the functional configuration of a pathway analysis device 10' in this case. In this Figure 7, the same reference numerals as those in Figure 1 have the same functions, so duplicated explanations will be omitted here. The pathway analysis device 10' shown in Figure 7 includes a symptom pathway input unit 14 instead of the disease pathway input unit 11 as a functional configuration.

[0059] The symptom pathway input unit 14 inputs a plurality of disease pathways generated for each of a plurality of symptoms related to a state change. A plurality of symptoms related to a state change is a combination of a plurality of symptoms that may progress from one symptom to another symptom for the same disease, such as a combination of microalbuminuria, albuminuria, and severe persistent proteinuria for diabetic nephropathy.

[0060] A symptom pathway is an intermolecular network generated by arranging, among molecules related to a specific symptom, causative molecules that act on the symptom on the upstream side of the pathway, responsive molecules that act on the symptom on the downstream side of the pathway, and arranging other molecules between the causative molecules and the responsive molecules. A symptom pathway can be generated by the same algorithm as a disease pathway.

[0061] When generating a symptom pathway using the same algorithm as the disease pathway, a symptom feature vector is calculated using the symptom names contained as words in multiple sentences. The symptom names used here are not limited to those linked to only a specific disease, but may also be linked to multiple different diseases (for example, edema is discussed in renal disease and liver disease, and proteinuria or decreased renal function is discussed in various renal and cardiac diseases).

[0062] When configured as in Figure 7, information is provided to show which parts from the start point to the end point of the pathway are common and which parts are not common in the relationship between the symptom pathway related to the symptom before the state change and the symptom pathway related to the symptom after the state change. Since the state change of the symptom appears as a partial change in the pathway from the start point to the end point of the pathway, by clearly showing the common and non-common parts in the pathway before and after the state change of the symptom, it is possible to understand the change in the pathway accompanying the state change of the symptom.

[0063] Second embodiment Next, a second embodiment of the present invention will be described with reference to the drawings. Fig. 8 is a block diagram showing an example of a functional configuration of a pathway analysis device 20 according to the second embodiment. In Fig. 8, the same reference numerals as those in Fig. 1 have the same functions, and therefore, redundant description will be omitted here.

[0064] As shown in Figure 8, the pathway analysis device 20 according to the second embodiment has, as its functional configuration, a disease pathway input unit 11, a partial common pathway detection unit 12, a common difference information providing unit 13, a symptom pathway input unit 21, a disease symptom common pathway detection unit 22, and a second common difference information providing unit 23.

[0065] The above-mentioned functional blocks 11 to 13, 21 to 23 can be configured by any of hardware, DSP, and software. For example, the above-mentioned functional blocks 11 to 13, 21 to 23 are realized by the operation of a program stored in a storage medium such as a RAM, a ROM, a hard disk, or a semiconductor memory under the control of a microcomputer configured with a CPU, a RAM, a ROM, etc.

[0066] The symptom pathway input unit 21 inputs a symptom pathway that represents the intermolecular interactions of molecules related to symptoms of a disease as a pathway diagram. Here, the disease refers to some or all of multiple diseases related to state changes, and is a disease related to the disease pathway input by the disease pathway input unit 11. When there are multiple symptoms related to a disease, a symptom pathway generated for one of the symptoms may be input, or a symptom pathway generated for each of multiple symptoms may be input.

[0067] The disease symptom common pathway detection unit 22 detects a pathway common to the disease pathway input by the disease pathway input unit 11 and the symptom pathway input by the symptom pathway input unit 21. The disease symptom common pathway detection unit 22 may detect only a portion of the pathway that is common from the starting point to the end point, as in the partial common pathway detection unit 12, or may detect a common portion without being limited to a portion.

[0068] Here, the disease symptom common pathway detection unit 22 detects a common pathway between a disease pathway related to disease A before a state change and a symptom pathway related to a symptom related to disease A. In addition, the disease symptom common pathway detection unit 22 detects a common pathway between a disease pathway related to disease B after a state change and a symptom pathway related to a symptom related to disease B.

[0069] The second common difference information providing unit 23 provides information in a state where the common pathway detected by the disease symptom common pathway detecting unit 22 and non-common pathways other than the common pathway can be distinguished from each other (a state where the common difference can be distinguished). The method of providing information may be the same as that of the common difference information providing unit 13, and may be any of a graph format, a spreadsheet format, and a text format.

[0070] FIG. 9 is a diagram showing an example of a common pathway detected by the disease symptom common pathway detection unit 22. Again, for the sake of simplicity, only some of the pathways included in the entire pathway are shown. FIG. 9(a) shows a disease pathway related to disease B, and FIG. 9(b) shows a symptom pathway related to one symptom related to disease B. The pathways in the rectangular frames in FIG. 9(a) and FIG. 9(b) are common pathways between the disease pathway and the symptom pathway.

[0071] As described above in the second embodiment, by detecting common pathways between disease pathways and symptom pathways and providing information in a state in which the common differences can be identified, it is possible to find which pathways in the disease pathway are related to the symptom pathway, or which pathways in the symptom pathway are related to the disease pathway.

[0072] This makes it possible to identify which connections in a disease network contribute deeply to which symptom manifestation, and which connections in a symptom network contribute deeply to which disease. In addition, since a common route between a disease pathway and a symptom pathway may appear as a commonality from the starting point to the end point, it is also possible to grasp important routes that are universally involved in the manifestation of symptoms that cause disease-specific symptoms.

[0073] In addition, the second common difference information providing unit 23 may be configured to represent common pathways and non-common pathways with the symptom pathway detected by the disease symptom common pathway detection unit 22 in a mutually distinguishable state, and to output a graph, spreadsheet, or text in which the partial common pathways and non-partial common pathways detected by the partial common pathway detection unit 12 are mutually distinguishable.

[0074] The disease symptom common pathway detection unit 22 may also focus on the partial common pathway or non-partial common pathway of the disease pathway detected by the partial common pathway detection unit 12, and detect a pathway common between the partial common pathway or non-partial common pathway and the symptom pathway. In this way, it is possible to analyze the relationship between the disease and the symptoms by focusing on a part that is considered to be a characteristic in the change in the state of the disease.

[0075] It should be noted that the above-mentioned embodiments are merely examples of the implementation of the present invention, and the technical scope of the present invention should not be interpreted as being limited thereby. In other words, the present invention can be implemented in various forms without departing from the gist or main characteristics thereof. [Explanation of symbols]

[0076] 10,10' Pathway Analysis Instrument 11 Disease pathway input section 12 Partial common path detection unit 13 Common difference information provision section 14 Symptom pathway input section 20 Pathway analysis device 21 Symptom pathway input section 22 Disease Symptom Common Pathway Detection Unit 23 Second common difference information provider

Claims

1. a partial common pathway detection unit which extracts only a portion of a pathway from a start point to an end point from each of a plurality of disease pathways or a plurality of symptom pathways, the partial common pathway detection unit being configured to detect a portion of the comparison target pathway that is common from the start point to the end point between a plurality of disease pathways that represent, as a pathway diagram, intermolecular interactions between molecules associated with a disease for each of a plurality of diseases related to a state change, or between a plurality of symptom pathways that represent, as a pathway diagram, intermolecular interactions between molecules associated with a symptom for each of a plurality of symptoms related to a state change; a common difference information providing unit that provides information on the partial common path detected by the partial common path detection unit and a non-partial common path other than the partial common path in a state in which the partial common path and the non-partial common path can be distinguished from each other. A pathway analysis device characterized by:

2. A partial common pathway detection unit that detects common parts only in a portion of the pathway from the start point to the end point between multiple disease pathways including one disease pathway that represents, as a pathway diagram, the intermolecular interactions of molecules related to one disease and another disease pathway that represents, as a pathway diagram, the intermolecular interactions of molecules related to another disease that may progress from the one disease, or between multiple symptom pathways including one symptom pathway that represents, as a pathway diagram, the intermolecular interactions of molecules related to one symptom and another symptom pathway that represents, as a pathway diagram, the intermolecular interactions of molecules related to another symptom that may progress from the one symptom; a common difference information providing unit that provides information on the partial common path detected by the partial common path detection unit and a non-partial common path other than the partial common path in a state in which the partial common path and the non-partial common path can be distinguished from each other. A pathway analysis device characterized by:

3. A partial common pathway detection unit which extracts only a portion of the pathway from the start point to the end point from each of the disease pathways or symptom pathways as a comparison target section between a plurality of disease pathways including one disease pathway which represents as a pathway diagram the intermolecular interactions of molecules related to one disease and another disease pathway which represents as a pathway diagram the intermolecular interactions of molecules related to another disease which may progress from the one disease, or between a plurality of symptom pathways including one symptom pathway which represents as a pathway diagram the intermolecular interactions of molecules related to one symptom and another symptom pathway which represents as a pathway diagram the intermolecular interactions of molecules related to another symptom which may progress from the one symptom, and detects a portion of the comparison target sections which are common from the start point to the end point as a partial common pathway; a common difference information providing unit that provides information on the partial common path detected by the partial common path detection unit and a non-partial common path other than the partial common path in a state in which the partial common path and the non-partial common path can be distinguished from each other. A pathway analysis device characterized by:

4. The pathway analysis device according to claim 2, characterized in that the partial common pathway detection unit divides the pathway from its starting point to its end point into an upstream section, a middle section, and a downstream section, and detects parts common to the plurality of disease pathways or parts common to the plurality of symptom pathways in only any of the divided sections.

5. The pathway analysis device of claim 1 or 3, characterized in that the partial common pathway detection unit divides the pathway from its starting point to its end point into an upstream section, a midstream section and a downstream section, extracts only one of the divided sections from each of the plurality of disease pathways or the plurality of symptom pathways as the comparison section, and detects as the partial common pathway a portion of the comparison section of the plurality of disease pathways that is common from its starting point to its end point, or a portion of the comparison section of the plurality of symptom pathways that is common from its starting point to its end point,

6. A partial common pathway detection unit that detects common parts only in a portion of a pathway from a starting point to an end point between a plurality of disease pathways that represent, as a pathway diagram, intermolecular interactions between molecules related to a disease for a plurality of diseases related to a state change, or between a plurality of symptom pathways that represent, as a pathway diagram, intermolecular interactions between molecules related to a symptom for a plurality of symptoms related to a state change; a common difference information providing unit that provides information on the partial common path detected by the partial common path detection unit and a non-partial common path other than the partial common path in a state in which the partial common path and the non-partial common path can be distinguished from each other, The partial common pathway detection unit divides the pathway from the starting point to the end point into an upstream section, a midstream section, and a downstream section, and detects a portion common to the plurality of disease pathways or a portion common to the plurality of symptom pathways only in any of the divided sections specified by a user. A pathway analysis device characterized by:

7. A partial common pathway detection unit that detects common parts only in a portion of a pathway from a starting point to an end point between a plurality of disease pathways that represent, as a pathway diagram, intermolecular interactions between molecules related to a disease for a plurality of diseases related to a state change, or between a plurality of symptom pathways that represent, as a pathway diagram, intermolecular interactions between molecules related to a symptom for a plurality of symptoms related to a state change; a common difference information providing unit that provides information on the partial common path detected by the partial common path detection unit and a non-partial common path other than the partial common path in a state in which the partial common path and the non-partial common path can be distinguished from each other, The partial common pathway detection unit divides the pathway from the starting point to the end point into an upstream section, a midstream section, and a downstream section, and detects a portion common to the plurality of disease pathways or a portion common to the plurality of symptom pathways in each of the upstream section, the midstream section, and the downstream section. A pathway analysis device characterized by:

8. A disease symptom common pathway detection unit that detects a common pathway between the disease pathway and the symptom pathway that represents, as a pathway diagram, intermolecular interactions of molecules related to symptoms of the disease; The present invention further includes a second common difference information providing unit that provides information on the common pathway detected by the disease symptom common pathway detection unit and a non-common pathway other than the common pathway in a state in which the common pathway can be distinguished from each other. The pathway analysis device according to any one of claims 1 to 4.

9. A disease symptom common pathway detection unit which extracts only a portion of the pathway from the start point to the end point as a comparison section from each of the disease pathway and the symptom pathway which represents the intermolecular interactions of molecules related to the symptoms of the disease as a pathway diagram, and detects a pathway that is common from the start point to the end point between the comparison sections as the partial common pathway; The method further includes a second common difference information providing unit that provides information on the partial common pathway detected by the disease symptom common pathway detecting unit and a non-partial common pathway other than the partial common pathway in a state where they can be distinguished from each other. The pathway analysis device according to any one of claims 1 to 4.

10. A partial common pathway detection unit that detects common parts only in a portion of a pathway from a starting point to an end point between a plurality of disease pathways that represent, as a pathway diagram, intermolecular interactions between molecules related to a disease for each of a plurality of diseases related to a state change, or between a plurality of symptom pathways that represent, as a pathway diagram, intermolecular interactions between molecules related to a symptom for each of a plurality of symptoms related to a state change; a common difference information providing unit that provides information on the partial common path detected by the partial common path detection unit and a non-partial common path other than the partial common path in a state in which the partial common path and the non-partial common path can be distinguished from each other; A disease symptom common pathway detection unit that detects a common pathway between the disease pathway and the symptom pathway that represents, as a pathway diagram, intermolecular interactions of molecules related to symptoms of the disease; a second common difference information providing unit that provides information on the common pathway detected by the disease symptom common pathway detecting unit and a non-common pathway other than the common pathway in a state in which the common pathway can be distinguished from each other; The disease symptom common pathway detection unit detects a pathway common between the partial common pathway or the non-partial common pathway detected by the partial common pathway detection unit for the disease pathway and the symptom pathway. A pathway analysis device characterized by:

11. a partial common pathway detection unit of the pathway analysis device extracts only a portion of a pathway from a start point to an end point from each of a plurality of disease pathways representing intermolecular interactions of molecules associated with a disease as a pathway diagram for each of a plurality of diseases related to a state change, or a plurality of symptom pathways representing intermolecular interactions of molecules associated with a symptom as a pathway diagram for each of a plurality of symptoms related to a state change, as a comparison target section, and detects a portion of the comparison target section that is common from the start point to the end point as a partial common pathway; a step in which the common difference information providing unit of the pathway analysis device provides information on the partial common pathway detected by the partial common pathway detection unit and a non-partial common pathway other than the partial common pathway in a state in which they can be distinguished from each other; A pathway analysis method comprising:

12. A partial common pathway detection unit of the pathway analysis device detects a common portion only in a portion from the start point to the end point of a pathway between multiple disease pathways including one disease pathway representing, as a pathway diagram, intermolecular interactions of molecules related to one disease and another disease pathway representing, as a pathway diagram, intermolecular interactions of molecules related to another disease which may progress from the one disease, or between multiple symptom pathways including one symptom pathway representing, as a pathway diagram, intermolecular interactions of molecules related to one symptom and another symptom pathway representing, as a pathway diagram, intermolecular interactions of molecules related to another symptom which may progress from the one symptom; a step in which the common difference information providing unit of the pathway analysis device provides information on the partial common pathway detected by the partial common pathway detection unit and a non-partial common pathway other than the partial common pathway in a state in which they can be distinguished from each other; A pathway analysis method comprising:

13. a partial common pathway detection means for extracting only a portion of a pathway from a start point to an end point from each of a plurality of disease pathways, each of which represents, as a pathway diagram, intermolecular interactions between molecules associated with a disease for each of a plurality of diseases related to a state change, or a plurality of symptom pathways, each of which represents, as a pathway diagram, intermolecular interactions between molecules associated with a symptom for each of a plurality of symptoms related to a state change, as a comparison target section, and detecting a portion from a start point to an end point among the comparison target sections as a partial common pathway; A common difference information providing means for providing information on the partial common path detected by the partial common path detection means and non-partial common paths other than the partial common path in a state in which they can be distinguished from each other. A pathway analysis program that allows computers to function as.

14. A partial common pathway detection means for detecting a common portion only in a part from a starting point to an end point of a pathway between a plurality of disease pathways including one disease pathway represented as a pathway diagram of intermolecular interactions of molecules related to one disease and another disease pathway represented as a pathway diagram of intermolecular interactions of molecules related to another disease which may progress from the one disease, or between a plurality of symptom pathways including one symptom pathway represented as a pathway diagram of intermolecular interactions of molecules related to one symptom and another symptom pathway represented as a pathway diagram of intermolecular interactions of molecules related to another symptom which may progress from the one symptom, and A common difference information providing means for providing information on the partial common path detected by the partial common path detection means and non-partial common paths other than the partial common path in a state in which they can be distinguished from each other. A pathway analysis program that allows computers to function as.

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