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

Abstract

The present invention comprises: a partially common path detection unit 12 that detects a portion common in only a part of a path from a start point to an end point among a plurality of disease pathways that represent a molecular interaction among disease-related molecules as a path diagram for each of a plurality of diseases related to a state change; and a common difference information providing unit 13 that executes information provision of the detected partially common path or a molecule included in this partially common path and a non-partially common path other than the partially common path or a molecule included in this non-partially common path in a state in which the provided information can be identified as information relating to the partially common path or information relating to the non-partially common path. By providing information such that, in a relationship between a disease pathway related to a disease before a state change and a disease pathway related to the disease after the state change, a common part of the paths from start points to end points and non-common parts of the paths are recognizable, the change in pathway caused by the state change of the disease can be recognized.

Description

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

[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.

[0002] Pathways (also called molecular networks) are known as diagrams that represent molecular interactions. Pathways are represented by symbols such as circles and squares, with arrows connecting the symbols to indicate molecular interactions. Visualizing molecular interactions in this way makes it easier to understand biological phenomena, for example by identifying which pathways contain genes with altered expression levels. Pathways are widely used in fields such as 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 that by determining a portion common to a plurality of molecule-function networks, it is estimated that a biomolecule that appears in the molecule-function network of the common portion is likely to play an important role in the expression of a bio-event.

[0004] Although a disease is sometimes perceived as a fixed state, in reality it undergoes various changes, such as the development of hepatocellular carcinoma from cirrhosis (non-alcoholic fatty liver disease (NAFLD) → non-alcoholic steatohepatitis (NASH) → hepatocellular carcinoma (HCC)), or the progression of diabetes to complications such as neuropathy, retinopathy, nephropathy, and arteriosclerosis.

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

[0006] Conventionally, it has been difficult to interpret such changes in the state of diseases or symptoms 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 range can be generated using various search functions, but there is no search function that focuses on interpreting changes in the state of diseases or symptoms as changes in intermolecular networks.

[0007] WO2003 / 077159 publication

[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).

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

[0010] According to the present invention configured as described above, information is provided that shows which parts of the disease pathway related to the disease before the state change and which parts of the disease pathway related to the disease after the state change are common and which parts are not common from the start point to the end point of the pathway.Since a change in the state of a disease appears as a partial change in the disease pathway from the start point to the end point of the pathway, by clearly indicating 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 associated with the state change of the disease.

[0011] Furthermore, according to the present invention, information is provided that allows one to understand which parts of the pathways from the start point to the end point 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 a state change of a symptom appears as a partial change in the symptom pathway from the start point to the end point of the pathway, by clearly indicating 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.

[0012] FIG. 1 is a block diagram showing an example of the functional configuration of a pathway analysis device according to a first embodiment. FIG. 2 is a block diagram showing an example of the functional configuration of a pathway generation device. FIG. 3 is a block diagram showing an example of the functional configuration of a feature vector calculation device. FIG. 4 is a diagram showing examples of a disease feature vector and a molecular feature vector. FIG. 5 is a diagram showing an example of partial common pathways detected in an upstream section by a partial common pathway detection unit. FIG. 6 is a diagram showing an example of information provided by a common difference information provision unit. FIG. 7 is a block diagram showing another example of the functional configuration of a pathway analysis device according to the first embodiment. FIG. 8 is a block diagram showing an example of the functional configuration of a pathway analysis device according to a second embodiment. FIG. 9 is a diagram showing an example of common pathways detected by a disease symptom common pathway detection unit.

[0013] (First Embodiment) A first embodiment of the present invention will now be described with reference to the drawings. Fig. 1 is a block diagram showing an example of the 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 its functional configuration, 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 using any of hardware, a 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 RAM, ROM, a hard disk, or a semiconductor memory under the control of a microcomputer configured with a CPU, RAM, ROM, etc. Note that these functional blocks 11 to 13 represent the processing procedure of the pathway analysis method.

[0015] The disease pathway input unit 11 inputs multiple pathways (hereinafter referred to as disease pathways) generated for multiple diseases related to a state change. Multiple diseases related to a state change are combinations of multiple 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 molecular interactions of disease-related molecules as a pathway diagram. That is, a disease pathway is a pathway diagram generated by placing disease-related molecules (genes or proteins) whose causative properties affecting the disease (hereinafter referred to as causative molecules) upstream of the pathway, molecules whose responsive properties affecting the disease (hereinafter referred to as responsive molecules) downstream of the pathway, and other molecules (hereinafter referred to as connecting molecules) between the causative and responsive molecules. Causality refers to the property that a molecule may cause disease through its presence or mutation. Responsiveness refers to the property that a molecule may mutate (change) due to the onset of a disease.

[0017] 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 multiple 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 estimate molecules related to a specific disease using a predetermined algorithm and use information on the properties of the actions of specific molecules on the disease estimated using the predetermined algorithm.

[0018] Any algorithm can be used as an algorithm for predicting associated molecules or for predicting molecular properties. For example, a prediction model based on machine learning using known information about the association between diseases and molecules can be used to predict new molecules associated with diseases. Furthermore, a prediction model based on machine learning using known information about the properties of molecules that act on diseases can be used to predict the properties of new molecules.

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

[0020] For example, pathways can be generated by an optimization process using a minimum flow algorithm based on the properties of each molecule and known information about the connectivity between molecules. Here, the connectivity between molecules includes, for example, a relationship in which an increase (or decrease) in the expression level of one molecule leads to a corresponding increase (or decrease) in the expression level of another molecule.

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

[0022] Below 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 feature vector identification unit 101 identifies a feature vector corresponding to a disease name (hereinafter referred to as a disease feature vector). The disease feature vector is data that represents the features of a disease (features that can identify a disease) as a combination of multiple element values. As an example, a vector that represents the contribution of a disease name contained as a word in multiple sentences to each sentence and to what extent is used as the disease feature vector.

[0024] While disease names as words tend to be used in sentences describing diseases, they tend not to be used in sentences unrelated to diseases. Furthermore, among sentences describing diseases, sentences containing a certain disease name as a word are likely to describe that disease, while sentences describing other types of diseases are likely to not contain that disease name. In other words, sentences containing disease names as words tend to differ depending on the type of disease they are about. Therefore, a vector representing the degree to which a disease name contributes to each sentence can be used as a feature vector capable of identifying 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 receives text data relating 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. The sentence analysis can be performed using, for example, a known morphological analysis. Note that the m sentences may contain multiple instances of the same word. In this case, the word extraction unit 201 does not extract multiple instances of the same word, but extracts only one. 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 converts each of the m sentences analyzed by the word extraction unit 201 into q-dimensional vectors according to a predetermined rule, thereby generating m sentence vectors d ｉ → (i=1, 2, ..., m) (the symbol "→" indicates a vector). 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 consisting of q axis components. ｊ → (j = 1, 2, ..., n) is calculated. ｉ → and word vector w ｊ →We will not explain the specific calculation method 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 sentence 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 a word matrix W, and calculates an index value matrix DW having m×n index values ​​as elements. ｔ is the transpose of the word matrix.

[0029]

[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 in the first row and second column 12 can be said to be a value that represents the degree to which word w2 contributes to sentence d1, and also a value that represents the degree to which sentence d1 contributes to 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 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 group for a word corresponding to a disease name among n sets of word index groups (m index values ​​per column) constituting each column of the index value matrix DW.

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

[0033] The molecular feature vector used here is data that represents the features of molecules such as proteins and genes (features that allow molecules to be identified) as a combination of multiple element values. As an example, a vector that represents the contribution of a molecule name contained as a word in multiple sentences to each sentence and to what extent 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 indexes 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 for a word corresponding to a molecular name among n sets of 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 using the feature vector calculation device 200. Then, machine learning of a first trained model is performed in advance using these datasets, 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 by various methods. 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 storage 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 dataset of disease feature vectors, molecular feature vectors, and property information representing the properties of molecules that act on the 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, with the causative molecule being located upstream and the responsive molecule being located downstream for multiple molecules whose association with a disease has been estimated by the related molecule estimation unit 102, and reflects the connection relationships shown in the knowledge database.

[0040] In this case, the pathway generation unit 104 generates the pathway by, for example, 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 value Th1 are placed upstream of the pathway, responsive molecules whose probability value is less than a second threshold value Th2 (Th1 > Th2) are placed downstream of the pathway, and connecting molecules whose probability value is greater than the second threshold value Th2 and less than the first threshold value 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 Figure 1, the partial common pathway detection unit 12 detects common parts between multiple disease pathways input by the disease pathway input unit 11, from the start point to the end point of the pathway. For example, the partial common pathway detection unit 12 divides the path from the start point to the end point into an upstream section, a midstream section, and a downstream section, and detects common parts between multiple disease pathways in only any of the divided sections. Such partially common pathways are called partial common pathways.

[0043] As an example, the second or third layer counting downstream from the start point of the route is the upstream section, the second or third layer counting 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. The user can specify, for example, 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] Figure 5 shows 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 simplicity, only a portion of the pathways included in the entire pathway (intermolecular network) is shown. In the pathway (part of the entire pathway) shown in Figure 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 tentatively used as identifiers for each molecule, and nodes with the same number represent 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, the two molecules 511 and 512 included in the partial pathway 501 shown in FIG. 5(a) are the same as the two molecules 513 and 514 included in the partial pathway 502 shown in FIG. 5(b). In the disease pathway for disease A shown in FIG. 5(a), the portion other than the partial common pathway 501 is a non-partial common pathway. In addition, in the disease pathway for disease B shown in FIG. 5(b), the portion other than the partial common pathway 502 is a non-partial common pathway.

[0046] In Figure 5(b), there is a causal molecule 515 in the first layer, and a path is connected from this causal molecule 515 to a connecting molecule 514 in the second layer, but such a path does not exist in Figure 5(a), and therefore is not detected as a partial common path. Furthermore, when processing to detect partial common paths is performed by specifying an upstream section, even if a partial common path exists in the midstream section or downstream section, it will not be 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 start point to the end point without dividing the path into an upstream section, a midstream section, and a downstream section, provided that the detected common portion 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, so that partial common pathways can be distinguished from other non-partial common pathways, as shown in Fig. 5. As an example of a distinguishable state, partial common pathways 501 and 502 can be surrounded by a rectangular frame, as shown in Fig. 5. Alternatively, the partial common pathways 501 and 502 may be highlighted or emphasized, or may be displayed in a color different from that of the non-partial common pathways.

[0050] In the example shown in Figure 5, it can be inferred that the causative molecule 215 acts as a stimulus on the second layer connecting molecule 514 in the disease pathway for disease A before the state change, causing a change in the midstream to downstream pathway, resulting in disease A progressing to disease B. In this case, it is possible to recognize the causative molecule 215 as a candidate drug discovery target molecule, for example.

[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 Figure 6, making the partial common pathway distinguishable from the other non-partial common pathways. As an example of a distinguishable state in this case, as shown in Figure 6(a), it is possible to highlight or color the names of molecules included in the partial common pathway within the entire pathway from the starting point to the end point. Alternatively or additionally, the names of molecules included in the partial common pathway and the names of molecules included in the non-partial common pathway may be displayed separately, as shown in Figure 6(b).

[0052] As described above, although only pathways that include partial common pathways are illustrated in Figure 5, other pathways that do not include partial common pathways also exist 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 that include partial common pathways. In other words, it is not necessary to provide information on other pathways that do not include partial common pathways.

[0053] As explained in detail above, in this embodiment, common parts from the start point to the end point of multiple disease pathways generated for multiple diseases related to state changes are detected, 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 this embodiment configured as described above, information is provided that shows which parts of the disease pathway related to the disease before the state change and which parts of the disease pathway related to the disease after the state change are common and which parts are not common from the start point to the end point of the pathway.Since a change in the state of a disease appears as a partial change in the pathway from the start point to the end point of the pathway, by clearly indicating 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 that accompanies the change in the state of the disease.

[0055] Furthermore, by detecting partial common pathways by specifying upstream, midstream, and downstream sections, it is possible to perform analysis that focuses on the information desired. For example, by specifying an upstream section and detecting a partial common pathway, it is possible to select candidate target molecules that may cause changes in responsiveness (disease phenotype) in diseases involving two state changes. Furthermore, by specifying a downstream section and detecting a partial common pathway, even if the responsiveness appears to be the same in diseases involving two state changes, it is possible to extract differences in causes (stimuli) and analyze the causes of state changes caused by the disease. Furthermore, by specifying a midstream section and detecting a partial common pathway, 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, any vector that represents the degree to which a disease name or molecule name contained as a word in a plurality of sentences contributes to each sentence is not limited to the feature vector calculated by the feature vector calculation device 200 shown in Fig. 3. Furthermore, it is not essential that the feature vector be calculated from the relationship between the sentence and the word, and any vector that can identify the features of a disease or molecule can be used as the disease feature vector and the molecule feature vector.

[0057] In the 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 used as 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 Figure 7, components with the same reference numerals as those in Figure 1 have the same functions, so redundant explanations will be omitted here. The pathway analysis device 10' shown in Figure 7 has 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 related to the same disease that may progress from one symptom to another, such as a combination of microalbuminuria, albuminuria, and severe, persistent proteinuria related to diabetic nephropathy.

[0060] A symptom pathway is a molecular network generated by placing causative molecules that act on a specific symptom upstream of the pathway, responsive molecules that act on the symptom downstream of the pathway, and other molecules between the causative and responsive molecules. Symptom pathways can be generated using the same algorithm as disease pathways.

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

[0062] When configured as shown in Figure 7, information is provided that shows which parts of the pathway from the start point to the end point 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 a change in the state of a symptom appears as a partial change in the pathway from the start point to the end point, by clearly indicating 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 that accompanies 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 the functional configuration of a pathway analysis device 20 according to the second embodiment. In Fig. 8, components with the same reference numerals as those shown 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 provision unit 13, a symptom pathway input unit 21, a disease symptom common pathway detection unit 22, and a second common difference information provision unit 23.

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

[0066] The symptom pathway input unit 21 inputs a symptom pathway that represents, as a pathway diagram, the intermolecular interactions of molecules related to symptoms of a disease. 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 symptom pathways 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 common to both the start and end points, as with 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 pathway common to the disease pathway related to disease A before the state change and the symptom pathway related to the symptoms related to disease A. In addition, the disease symptom common pathway detection unit 22 detects a pathway common to the disease pathway related to disease B after the state change and the symptom pathway related to the symptoms related to disease B.

[0069] The second common difference information providing unit 23 provides information in a state in which 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 in which common differences 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 in any of a graph format, a spreadsheet format, or a text format.

[0070] Figure 9 is a diagram showing an example of a common pathway detected by the disease symptom common pathway detection unit 22. Again, for simplicity of explanation, only a portion of the pathways included in the entire pathway is shown. Figure 9(a) shows a disease pathway related to disease B, and Figure 9(b) shows a symptom pathway related to one symptom related to disease B. The pathways enclosed by rectangular frames in Figures 9(a) and 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 becomes 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 the manifestation of which symptoms, and which connections in a symptom network contribute deeply to which diseases. Furthermore, because common pathways between disease pathways and symptom pathways may appear as commonalities from the starting point to the end point, it is also possible to identify important pathways 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 represent the 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 for the disease pathway, and may 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] Furthermore, the disease-symptom common pathway detection unit 22 may 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 symptoms by focusing on parts that are considered to be characteristic in the change in the state of the disease.

[0075] It should be noted that the above-described embodiments are merely examples of specific embodiments for carrying out the present invention, and the technical scope of the present invention should not be construed as being limited thereby. In other words, the present invention can be carried out in various forms without departing from the gist or main features thereof.

[0076] 10, 10' Pathway analysis device 11 Disease pathway input unit 12 Partial common pathway detection unit 13 Common difference information providing unit 14 Symptom pathway input unit 20 Pathway analysis device 21 Symptom pathway input unit 22 Disease symptom common pathway detection unit 23 Second common difference information providing unit

Claims

1. A pathway analysis device comprising: 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 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; and a common difference information providing unit that provides information on the partial common pathways detected by the partial common pathway detection unit and non-partial common pathways other than the partial common pathways in a state in which they can be distinguished from each other.

2. The pathway analysis device of claim 1, wherein 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 the parts common to the multiple disease pathways or the parts common to the multiple symptom pathways in only any of the divided sections.

3. The pathway analysis device according to claim 1 or 2, further comprising: a disease symptom common pathway detection unit that detects common pathways between 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 a second common difference information providing unit that provides information on the common pathway detected by the disease symptom common pathway detection unit and non-common pathways other than the common pathway in a state in which they can be distinguished from each other.

4. The pathway analysis device described in claim 3, characterized in that the disease symptom common pathway detection unit detects common pathways 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.

5. A pathway analysis method comprising the steps of: a partial common pathway detection unit of a pathway analysis device detecting a common portion between a plurality of disease pathways, each of which represents, as a pathway diagram, the intermolecular interactions of molecules related to a disease for each of a plurality of diseases related to a state change; or between a plurality of symptom pathways, each of which represents, as a pathway diagram, the intermolecular interactions of molecules related to a symptom for each of a plurality of symptoms related to a state change; and a common difference information provision unit of the pathway analysis device providing information on the partial common pathways detected by the partial common pathway detection unit and non-partial common pathways other than the partial common pathways in a state in which they can be distinguished from each other.

6. A pathway analysis program that causes a computer to function as a partial common pathway detection means that 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 each of a plurality of diseases related to a change in condition, or between a plurality of symptom pathways that represent, as a pathway diagram, the intermolecular interactions of molecules related to a symptom for each of a plurality of symptoms related to a change in condition, and a common difference information providing means that provides information on the partial common pathways detected by the partial common pathway detection means in a state in which they can be distinguished from non-partial common pathways other than the partial common pathways.

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