Information processing program, information processing method, and information processing device

Abstract

This information processing device trains a learning model on the basis of training data that defines the relationship between a vector corresponding to a genome and vectors respectively corresponding to a plurality of subgenomes constituting the genome. Upon receiving a genome to be analyzed, the information processing device inputs the genome to be analyzed into the learning model to calculate vectors for a plurality of subgenomes that correspond to the genome to be analyzed.

Description

[Technical field]

[0001] The present invention relates to an information processing program and the like. [Background technology]

[0002] With the advancement of gene transfer technology and the deepening understanding of immune mechanisms, gene manipulation is being carried out using gene vectors. Depending on the size of the gene fragment to be inserted and the purpose of the insertion, vehicles with various added characteristics are used as gene vectors. Gene vectors derived from E. coli, yeast, host organisms, etc. are used for such manipulations.

[0003] For example, chimeric antigen receptor (CAR)-introduced T cell therapy has attracted attention as a cancer immunotherapy using genetically modified T cells. CAR is an artificial receptor that can specifically recognize and attack cancer antigens, and is artificially created by combining an antibody-derived part that specifically recognizes antigens with a cytotoxic functional part derived from TCR (T cell receptor). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2020 / 230240 [Patent Document 2] International Publication No. 2007 / 102578 Summary of the Invention [Problem to be solved by the invention]

[0005] Although the development of gene therapy drugs using gene vectors is very promising, it is difficult to synthesize gene therapy drugs using the wide variety of gene vectors as they are.

[0006] Therefore, it is conceivable to synthesize the desired gene therapy drug by using a wide variety of gene vectors as substitutes, but the current situation is that it is difficult to find substitute gene vectors and to efficiently modify genes.

[0007] In one aspect, the present invention aims to provide an information processing program, an information processing method, and an information processing device that can identify a genome that serves as a substitute for a subgenome contained in a genome of interest. [Means for solving the problem]

[0008] In the first proposal, the computer executes the following process: The computer executes learning of a learning model based on learning data that defines the relationship between a vector corresponding to a genome and vectors corresponding to each of the multiple subgenomes that make up the genome. When the computer receives a genome to be analyzed, it inputs the genome to be analyzed into the learning model to calculate vectors of the multiple subgenomes that correspond to the genome to be analyzed. Effect of the Invention

[0009] Alternative genomes to subgenomes contained within the genome of interest can be identified. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram for explaining a genome. [Diagram 2] FIG. 2 is a diagram showing the relationship between amino acids, bases, and codons. [Diagram 3] FIG. 3 is a diagram for explaining the primary, secondary, tertiary and higher-order structures of a protein. [Figure 4] FIG. 4 is a diagram showing an example of a gene vector. [Diagram 5] FIG. 5 is a diagram illustrating an example of a process in the learning phase of the information processing device according to the present embodiment. [Figure 6]FIG. 6 is a diagram illustrating an example of a process in the analysis phase of the information processing device according to the present embodiment. [Figure 7] FIG. 7 is a functional block diagram of the information processing device according to the first embodiment. As shown in FIG. [Figure 8] FIG. 8 is a diagram illustrating an example of the data structure of a base file. [Figure 9] FIG. 9 is a diagram illustrating an example of a data structure of the conversion table. [Figure 10] FIG. 10 is a diagram illustrating an example of a data structure of the dictionary table. [Figure 11] FIG. 11 is a diagram showing an example of the data structure of the protein primary structure dictionary. [Figure 12] FIG. 12 is a diagram illustrating an example of a data structure of the secondary structure dictionary. [Figure 13] FIG. 13 is a diagram showing an example of the data structure of the tertiary structure dictionary. [Figure 14] FIG. 14 is a diagram showing an example of the data structure of the higher-order structure dictionary. [Figure 15] FIG. 15 illustrates an example of the data structure of the compressed file table. [Figure 16] FIG. 16 is a diagram illustrating an example of a data structure of the vector table. [Figure 17] FIG. 17 is a diagram showing an example of the data structure of the protein primary structure vector table. [Figure 18] FIG. 18 is a diagram illustrating an example of a data structure of the secondary structure vector table. [Figure 19] FIG. 19 is a diagram showing an example of the data structure of the tertiary structure vector table. [Figure 20] FIG. 20 is a diagram showing an example of the data structure of the higher-order structure vector table. [Figure 21] FIG. 21 is a diagram illustrating an example of a data structure of the inverted index table. [Figure 22] FIG. 22 is a diagram showing an example of a data structure of a protein primary structure transposition index. [Figure 23]FIG. 23 is a diagram showing an example of a data structure of a secondary structure inverted index. [Figure 24] FIG. 24 is a diagram showing an example of the data structure of a tertiary structure inverted index. [Diagram 25] FIG. 25 is a diagram showing an example of the data structure of a higher-order structure inverted index. [Figure 26] FIG. 26 is a diagram illustrating an example of a data structure of the genome dictionary. [Figure 27] FIG. 27 is a flowchart (1) showing the processing procedure of the information processing device according to the present embodiment. [Figure 28] FIG. 28 is a flowchart (2) showing the processing procedure of the information processing device according to the present embodiment. [Figure 29] FIG. 29 is a diagram illustrating an example of the process of the learning phase of the information processing device according to the second embodiment. [Diagram 30] FIG. 30 is a diagram for explaining the process of the information processing device according to the second embodiment. [Diagram 31] FIG. 31 is a functional block diagram of an information processing device according to the second embodiment. As shown in FIG. [Diagram 32] FIG. 32 is a flowchart of a processing procedure of the information processing device according to the second embodiment. [Diagram 33] FIG. 33 is a diagram illustrating an example of a hardware configuration of a computer that realizes the same functions as the information processing apparatus of the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an information processing program, an information processing method, and an information processing device disclosed in the present application will be described in detail with reference to the accompanying drawings. Note that the present invention is not limited to the embodiments. EXAMPLES

[0012] Before describing this embodiment, the genome will be described. FIG. 1 is a diagram for explaining the genome. The genome 1 includes genetic information that specifies the order in which a plurality of amino acids are linked. Here, an amino acid is determined by three consecutive bases, that is, a codon. The genome 1 also includes information on the protein 1a. The protein 1a is a chain in which a plurality of 20 types of amino acids are linked. The structure of the protein 1a can be understood as the primary structure, secondary structure, tertiary structure, and higher-order (quaternary) structure of the protein. FIG. 1b shows the higher-order structure of the protein 1a. In the following description, the primary structure of the protein, the secondary structure of the protein, the tertiary structure of the protein, and the higher-order structure of the protein will be appropriately expressed as the primary structure, the secondary structure, the tertiary structure, and the higher-order structure of the protein, respectively.

[0013] DNA and RNA have four types of bases, represented by the symbols "A," "G," "C," "T," or "U." Furthermore, a group of three base sequences determines 20 types of amino acids. Each amino acid is represented by the symbols "A" through "Y." Figure 2 shows the relationship between amino acids, bases, and codons. A group of three base sequences is called a "codon." The order of each base determines a codon, and once the codon is determined, the amino acid is determined.

[0014] As shown in Figure 2, multiple types of codons correspond to one amino acid. Therefore, when a codon is determined, the amino acid is also determined, but the codon is not uniquely specified even if the amino acid is determined. For example, the amino acid "alanine (Ala) A" corresponds to the codons "GCU", "GCC", "GCA", or "GCG".

[0015] Moreover, a protein is uniquely determined by a base sequence. The primary structure of a protein is a sequence of a plurality of amino acids. The secondary structure includes an α-helix and a β-sheet, which are symmetrical substructures observed locally. The tertiary structure includes a plurality of secondary structures. Furthermore, the higher-order structure includes a plurality of tertiary structures. FIG. 3 is a diagram for explaining the primary structure, secondary structure, tertiary structure, and higher-order structure of a protein. For example, as shown in FIG. 3, the higher-order structure Z1 includes tertiary structures Y1, Y2, Y3, etc. The tertiary structure Y1 includes secondary structures X1, X2, X3, etc. The secondary structure X1 includes primary structures W1, W2, W3, etc. The primary structure W1 includes amino acids A1, A2, A3, etc.

[0016] The gene vector used in this embodiment is a DNA or RNA molecule used to artificially carry foreign genetic material into another cell. Gene vectors include plasmids, cosmids, lambda phages, and artificial chromosomes. FIG. 4 is a diagram showing an example of a gene vector. The gene vector shown in FIG. 4 is a pBR322 plasmid, which is widely used as a cloning vector. The gene vector itself is a DNA and RNA base sequence, and will be described as corresponding to the higher-order structure of the protein described in FIG. 3, for example.

[0017] Moreover, a gene vector is generated by synthesizing a plurality of sub-vectors. The sub-vectors are DNA and RNA base sequences, and correspond, for example, to the secondary structure of a protein described in FIG. 3. Sub-vectors include so-called E. coli vectors that contain elements necessary for maintenance in E. coli, and vectors for maintenance in cell systems derived from yeast, plants, mammals, etc. The sub-vectors may be other vectors.

[0018] Next, an example of the process of the information processing device according to the present embodiment will be described.

[0019] Fig. 5 is a diagram for explaining an example of a learning phase process of the information processing device according to the present embodiment. As shown in Fig. 5, the information processing device executes machine learning of a learning model 70 using learning data 65. The learning model 70 corresponds to a CNN (Convolutional Neural Network), an RNN (Recurrent Neural Network), or the like.

[0020] The learning data 65 defines the relationship between the vector of the target genome (therapeutic drug) and the vectors of multiple subgenomes contained in the target genome. For example, the vector of the target genome corresponds to the input data, and the multiple subgenomes are the correct answer values ​​of the output data.

[0021] The information processing device performs learning by backpropagation so that the output when the vector of the target genome is input to the learning model 70 approaches the vector of each subgenome. The information processing device adjusts the parameters of the learning model 70 (performs machine learning) by repeatedly performing the above process based on the relationship between the vector of the target genome included in the learning data 65 and the vectors of multiple subgenomes.

[0022] 6 is a diagram for explaining an example of a process in the analysis phase of the information processing device according to the present embodiment. In the analysis phase, the information processing device executes the following process using the learning model 70 learned in the learning phase.

[0023] When the information processing device receives an analysis query 80 specifying a target genome (therapeutic drug), it converts the target genome of the analysis query 80 into a vector Vob80. The information processing device inputs the vector Vob80 into a learning model 70 to calculate a plurality of vectors (Vsb80-1, Vsb80-2, Vsb80-3, ... Vsb80-n) corresponding to each subgenome, and stores them in a subgenome table T1.

[0024] The information processing device compares the similarity between multiple vectors (Vt1, Vt2, Vt3, ... Vtn) corresponding to each alternative gene vector stored in the alternative gene vector table T2 and multiple vectors (Vsb80-1, Vsb80-2, Vsb80-3, ... Vsb80-n) to identify vectors of similar alternative gene vectors. The information processing device associates the vectors of the target genome, the vectors of the subgenome, and the vectors of the similar alternative gene vectors, and registers them in the alternative management table 85.

[0025] As described above, the information processing device according to this embodiment performs learning of the learning model 70 based on the learning data 65 that defines the relationship between the vector of the target genome and the vector of each subgenome. The information processing device calculates the vector of each subgenome corresponding to the target genome of the analysis query by inputting the vector of the analysis query to the trained learning model 70. By using the vector of each subgenome output from the learning model 70, it is possible to easily detect a gene vector that is similar to the subgenome contained in the target genome and is replaceable.

[0026] Next, an example of the configuration of the information processing device according to the present embodiment 1 will be described. Fig. 7 is a functional block diagram showing the configuration of the information processing device according to the present embodiment 1. As shown in Fig. 7, the information processing device 100 includes a communication unit 110, an input unit 120, a display unit 130, a storage unit 140, and a control unit 150.

[0027] The communication unit 110 is connected to an external device or the like by wire or wirelessly, and transmits and receives information to and from the external device or the like. For example, the communication unit 110 is realized by a NIC (Network Interface Card) or the like. The communication unit 110 may be connected to a network (not shown).

[0028] The input unit 120 is an input device that inputs various types of information to the information processing device 100. The input unit 120 corresponds to a keyboard, a mouse, a touch panel, or the like.

[0029] The display unit 130 is a display device that displays information output from the control unit 150. The display unit 130 corresponds to a liquid crystal display, an organic EL (Electro Luminescence) display, a touch panel, or the like.

[0030] The storage unit 140 has a base file 50, a conversion table 51, a dictionary table 52, a compressed file table 53, a vector table 54, and a transposed index table 55. The storage unit 140 also has a subgenome table T1, an alternative gene vector table T2, a genome dictionary D2, learning data 65, a learning model 70, an analysis query 80, and an alternative management table 85. The storage unit 140 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk.

[0031] The base file 50 is a file that holds information in which multiple bases are arranged. Fig. 8 is a diagram showing an example of the data structure of a base file. As shown in Fig. 8, the base file 50 has four types, each of which is represented by the symbol "A", "G", "C", "T" or "U".

[0032] Conversion table 51 is a table that associates codons with their codes. A group of three base sequences is called a "codon." FIG. 9 is a diagram showing an example of the data structure of a conversion table. As shown in FIG. 9, each codon is associated with each code. For example, the code for the codon "UUU" is "40h (01000000)." "h" indicates a hexadecimal number.

[0033] The dictionary table 52 is a table that holds various dictionaries. Fig. 10 is a diagram showing an example of the data structure of the dictionary table. As shown in Fig. 10, the dictionary table 52 has a protein primary structure dictionary D1-1, a secondary structure dictionary D1-2, a tertiary structure dictionary D1-3, and a higher-order structure dictionary D1-4.

[0034] The protein primary structure dictionary D1-1 is dictionary data that defines the relationship between the compressed code of a protein and the sequence of codons that constitute the protein. FIG. 11 is a diagram showing an example of the data structure of the protein primary structure dictionary. As shown in FIG. 11, the protein primary structure dictionary D1-1 associates a compressed code, a name, and a codon code sequence. A compressed code is a compressed code sequence of codons (or a symbol sequence of amino acids). A name is the name of a protein. A codon code sequence is a sequence of compressed codes of codons. Note that instead of a codon code sequence, a sequence of amino acid symbols may be associated with the compressed code of a protein primary structure.

[0035] For example, the protein primary structure "type 1 collagen" is assigned the compressed code "C0008000h." The codon code sequence corresponding to the compressed code "C0008000h" is "02h63h78h...03h."

[0036] The secondary structure dictionary D1-2 is dictionary data that defines the relationship between the sequence of compressed codes of protein primary structures and the compressed codes of secondary structures. Fig. 12 is a diagram showing an example of the data structure of the secondary structure dictionary. As shown in Fig. 12, the secondary structure dictionary D1-2 associates compressed codes, names, and protein primary structure code sequences. The compressed codes are compressed codes assigned to the secondary structures of proteins. The names are the names of the secondary structures. The protein primary structure code sequences are compressed code sequences of protein primary structures corresponding to the secondary structures.

[0037] For example, the secondary structure "α secondary structure" is assigned the compressed code "D0000000h." The protein primary structure code sequence corresponding to the compressed code "D0000000h" is "C0008001hC00...".

[0038] The tertiary structure dictionary D1-3 is dictionary data that defines the relationship between the arrangement of compressed codes of secondary structures and the compressed codes of tertiary structures. Fig. 13 is a diagram showing an example of the data structure of the tertiary structure dictionary. As shown in Fig. 13, the tertiary structure dictionary D1-3 associates compressed codes, names, and secondary structure code arrangements. The compressed codes are compressed codes assigned to the tertiary structures. The names are the names of the tertiary structures. The secondary structure code arrangements are arrangements of compressed codes of secondary structures corresponding to the tertiary structures.

[0039] For example, the tertiary structure "αα tertiary structure" is assigned the compressed code "E0000000h." The secondary structure code sequence corresponding to the compressed code "E0000000h" is "D0008031hD00...".

[0040] The higher-order structure dictionary D1-4 is dictionary data that defines the relationship between the arrangement of compressed codes of the tertiary structure and the compressed codes of the higher-order structure. Fig. 14 is a diagram showing an example of the data structure of the higher-order structure dictionary. As shown in Fig. 14, the higher-order structure dictionary D1-4 associates compressed codes, names, and tertiary structure code arrangements. The compressed codes are compressed codes assigned to the higher-order structures. The names are the names of the higher-order structures. The tertiary structure code arrangements are arrangements of compressed codes of the tertiary structure corresponding to the higher-order structures.

[0041] For example, the higher-order structure "ααα higher-order structure" is assigned the compressed code "F0000000h." The tertiary structure code sequence corresponding to the compressed code "F0000000h" is "E0000031hE00...".

[0042] Returning to the explanation of Fig. 7, the compressed file table 53 is a table that holds various compressed files. Fig. 15 is a diagram showing an example of the data structure of the compressed file table. As shown in Fig. 15, this compressed file table 53 has a codon compressed file 53A, a protein primary structure compressed file 53B, a secondary structure compressed file 53C, a tertiary structure compressed file 53D, and a higher-order structure compressed file 53E.

[0043] The codon compressed file 53A is a file in which the bases included in the base file 50 are compressed in units of codons.

[0044] The protein primary structure compressed file 53B is a file in which the sequence of compressed codes of the codons included in the codon compressed file 53A is encoded in units of protein primary structures.

[0045] The secondary structure compressed file 53C is a file obtained by encoding the sequence of compressed codes of the protein primary structures included in the protein primary structure compressed file 53B in units of secondary structures.

[0046] The tertiary structure compressed file 53D is a file obtained by encoding the arrangement of compressed codes of the secondary structures included in the secondary structure compressed file 53C in units of tertiary structures.

[0047] The higher-order structure compressed file 53E is a file obtained by encoding the arrangement of compressed codes of the tertiary structures included in the tertiary structure compressed file 53D in units of higher-order structures.

[0048] The vector table 54 is a table that holds vectors corresponding to a protein primary structure, secondary structure, tertiary structure, and higher-order structure. Fig. 16 is a diagram showing an example of the data structure of the vector table. As shown in Fig. 16, the vector table 54 has a protein primary structure vector table VT1-1, a secondary structure vector table VT1-2, a tertiary structure vector table VT1-3, and a higher-order structure vector table VT1-4.

[0049] The protein primary structure vector table VT1-1 is a table that holds vectors corresponding to the protein primary structure. Fig. 17 is a diagram showing an example of the data structure of the protein primary structure vector table. As shown in Fig. 17, the protein primary structure vector table VT1-1 associates the compressed code of the protein primary structure with the vector assigned to the compressed code of the protein primary structure. The vector of the protein primary structure is calculated by Poincaré embedding. The Poincaré embedding will be described later.

[0050] The secondary structure vector table VT1-2 is a table that holds vectors corresponding to secondary structures. Fig. 18 is a diagram showing an example of the data structure of the secondary structure vector table. As shown in Fig. 18, the secondary structure vector table VT1-2 associates the compressed code of the secondary structure with the vector assigned to the compressed code of the secondary structure. The vector of the secondary structure is calculated by accumulating the vector of the protein primary structure included in the secondary structure.

[0051] The tertiary structure vector table VT1-3 is a table that holds vectors corresponding to tertiary structures. Fig. 19 is a diagram showing an example of the data structure of the tertiary structure vector table. As shown in Fig. 19, the tertiary structure vector table VT1-3 associates the compressed code of the tertiary structure with the vector assigned to the compressed code of the tertiary structure. The vector of the tertiary structure is calculated by multiplying the vector of the secondary structure included in the tertiary structure.

[0052] The higher-order structure vector table VT1-4 is a table that holds vectors corresponding to higher-order structures. Fig. 20 is a diagram showing an example of the data structure of the higher-order structure vector table. As shown in Fig. 20, the higher-order structure vector table VT1-4 associates the compressed code of the higher-order structure with the vector assigned to the compressed code of the higher-order structure. The vector of the higher-order structure is calculated by accumulating the vector of the tertiary structure included in the higher-order structure.

[0053] Returning to the explanation of Fig. 7, the inverted index table 55 is a table that holds various inverted indexes. Fig. 21 is a diagram showing an example of the data structure of the inverted index table. As shown in Fig. 21, the inverted index table 55 has a protein primary structure inverted index In1-1, a secondary structure inverted index In1-2, a tertiary structure inverted index In1-3, and a higher order structure inverted index In1-4.

[0054] 22 is a diagram showing an example of the data structure of a protein primary structure transposition index. The horizontal axis of the protein primary structure transposition index In1-1 corresponds to the offset. The vertical axis of the protein primary structure transposition index In1-1 corresponds to the compressed code of the protein primary structure. The protein primary structure transposition index In1-1 is represented by a bitmap of "0" or "1", and all bitmaps are set to "0" in the initial state.

[0055] For example, the offset of the compressed code of the protein primary structure at the beginning of the protein primary structure compressed file 53B is set to "0." If the protein primary structure code "C0008000h (type I collagen)" is included in the eighth position from the beginning of the protein primary structure compressed file 53B, the bit at the position where the column of offset "7" of the protein transposition index In1-1 intersects with the row of the protein code "C0008000h (type I collagen)" is set to "1."

[0056] 23 is a diagram showing an example of a data structure of a secondary structure transposition index. The horizontal axis of the secondary structure transposition index In1-2 corresponds to the offset. The vertical axis of the secondary structure transposition index In1-2 corresponds to the compressed code of the secondary structure. The secondary structure transposition index In1-2 is represented by a bitmap of "0" or "1", and all bitmaps are set to "0" in the initial state.

[0057] For example, the offset of the compressed code of the secondary structure at the beginning of the secondary structure compressed file 53C is set to "0." If the secondary structure code "D000000h (α secondary structure)" is included in the eighth position from the beginning of the secondary structure compressed file 53C, the bit at the position where the column of the offset "7" of the secondary structure transposition index In1-2 intersects with the row of the compressed code of the secondary structure "D0000000h (α secondary structure)" is set to "1."

[0058] 24 is a diagram showing an example of the data structure of a tertiary structure inverted index. The horizontal axis of the tertiary structure inverted indexes In1-3 corresponds to the offset. The vertical axis of the tertiary structure inverted indexes In1-3 corresponds to the compressed code of the tertiary structure. The tertiary structure inverted indexes In1-3 are represented by a bitmap of "0" or "1", and all bitmaps are set to "0" in the initial state.

[0059] For example, the offset of the compressed code of the tertiary structure at the beginning of the tertiary structure compressed file 53D is set to "0." If the tertiary structure code "E0000000h (αα tertiary structure)" is included in the 11th position from the beginning of the tertiary structure compressed file 53D, the bit at the position where the column of the offset "10" of the tertiary structure transposition index In1-3 intersects with the row of the tertiary structure compressed code "E0000000h (αα tertiary structure)" is set to "1."

[0060] 25 is a diagram showing an example of the data structure of a higher-order structure inverted index. It is a diagram showing an example of the data structure of a higher-order structure inverted index. The horizontal axis of the higher-order structure inverted indexes In1-4 is an axis corresponding to the offset. The vertical axis of the higher-order structure inverted indexes In1-4 is an axis corresponding to the compressed code of the higher-order structure. The higher-order structure inverted indexes In1-4 are represented by a bitmap of "0" or "1", and all bitmaps are set to "0" in the initial state.

[0061] For example, the offset of the compressed code of the higher-order structure at the beginning of the higher-order structure compressed file 53E is set to "0." If the higher-order structure code "F0000000h (ααα higher-order structure)" is included in the 11th position from the beginning of the higher-order structure compressed file 53E, the bit at the position where the column of the offset "10" of the higher-order structure transposition index In1-4 intersects with the row of the compressed code of the higher-order structure "F0000000h (ααα higher-order structure)" is set to "1."

[0062] Returning to the explanation of FIG. 7, the alternative gene vector table T2 holds vectors of multiple gene vectors. A gene vector corresponds to a secondary structure of a protein. For example, a vector stored in the alternative gene vector table T2 may be a vector registered in the two-dimensional structure vector table VT1-2. As explained in FIG. 6, the data structure of the alternative gene vector table T2 stores vectors of multiple alternative gene vectors.

[0063] The genome dictionary D2 defines the relationship between the name of a target genome and the names of subgenomes contained in the target genome. Figure 26 is a diagram showing an example of the data structure of the genome dictionary. As shown in Figure 26, the genome dictionary D2 associates the name of a target vector with the names of multiple subgenomes.

[0064] The training data 65 defines the relationship between the vector of the target genome and the vectors of multiple subgenomes contained in this target genome. The data structure of the training data 65 corresponds to the data structure of the training data described in FIG.

[0065] The learning model 70 is a model corresponding to CNN, RNN, or the like, and parameters are set.

[0066] The analysis query 80 includes information on a target genome (therapeutic drug) to be analyzed. For example, the information on the target genome includes information on a base sequence corresponding to a higher-order structure.

[0067] The substitution management table 85 is a table that holds vectors of subgenomes contained in a target genome in association with gene vectors that are similar to the subgenomes and can be substituted.

[0068] The control unit 150 includes a preprocessing unit 151, a learning unit 152, a calculation unit 153, and an analysis unit 154. The control unit 150 is realized by, for example, a central processing unit (CPU) or a micro processing unit (MPU). The control unit 150 may also be executed by, for example, an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0069] The preprocessing unit 151 executes the various processes described below to calculate a vector of a higher-order structure or a tertiary structure corresponding to the target genome (therapeutic drug), a vector of a secondary structure corresponding to the subgenome, and the like.

[0070] First, the preprocessing unit 151 executes a process of generating a codon compressed file 53A, a process of generating a protein primary structure compressed file 53B, and a process of generating a protein primary structure vector table VT1-1 and a protein primary structure inversion index In1-1.

[0071] The preprocessing unit 151 compares the base file 50 with the conversion table 51, assigns compressed codes to the base sequences in the base file 50 in units of codons, and generates a codon-compressed file 53A.

[0072] The preprocessing unit 151 compares the codon compressed file 53A with the protein primary structure dictionary D1-1, assigns compressed codes to the sequence of compressed codes of the codons included in the codon compressed file 53A in units of the protein primary structure, and generates the protein compressed file 53B.

[0073] When the preprocessing unit 151 generates the protein primary structure compressed file 53B, the preprocessing unit 151 embeds the compressed code of the protein primary structure into the Poincare space to calculate a vector of the protein primary structure (compressed code of the protein primary structure). The process of embedding into the Poincare space and calculating a vector is a technique called Poincare Embeddings. For example, the technique described in the non-patent document "Valentin Khrulkov1 et al. "Hyperbolic Image Embeddings" Cornell University, April 3, 2019" may be used for Poincare Embedding.

[0074] In the Poincaré embedding, a vector is assigned according to the position embedded in the Poincaré space, and the more similar the information, the closer the position of the information is to be embedded. Therefore, groups having similar characteristics are embedded in positions close to each other in the Poincaré space, and similar vectors are assigned to them. Although not shown, the pre-processing unit 151 refers to a protein primary structure similarity table that defines similar protein primary structures, embeds the compressed code of each protein primary structure in the Poincaré space, and calculates the vector of the compressed code of each protein primary structure. The pre-processing unit 151 may perform the Poincaré embedding in advance for the compressed code of each protein primary structure defined in the protein dictionary primary structure D1-1.

[0075] The preprocessing unit 151 generates a protein primary structure vector table VT1-1 by associating the protein primary structure (compressed code of the protein primary structure) with the vector of the protein primary structure. The preprocessing unit 151 generates a protein primary structure transposition index In1-1 based on the relationship between the vector of the protein primary structure and the position of the protein primary structure (compressed code of the protein primary structure) in the protein primary structure compressed file 53B.

[0076] Next, the preprocessing unit 151 executes a process of generating a secondary structure compressed file 53C, a secondary structure vector table VT1-2, and a secondary structure inverted index In1-2.

[0077] The preprocessing unit 151 compares the protein primary structure compressed file 53B with the secondary structure dictionary D1-2, assigns compression codes to the sequence of compression codes of the protein primary structures contained in the protein primary structure compressed file 53B on a secondary structure basis, and generates the secondary structure compressed file 53C.

[0078] The preprocessing unit 151 refers to the secondary structure dictionary D1-2 to identify a protein primary structure code sequence (array of compressed codes of protein primary structures) corresponding to the compressed codes of the secondary structures. The preprocessing unit 151 obtains vectors of compressed codes of each identified protein primary structure from the protein primary structure vector table VT1-1, and calculates vectors of compressed codes of secondary structures by adding each obtained vector. The preprocessing unit 151 calculates vectors of each secondary structure by repeatedly executing the above process.

[0079] The preprocessing unit 151 generates a secondary structure vector table VT1-2 by associating the secondary structure (compressed code of the secondary structure) with the vector of the secondary structure. The preprocessing unit 151 generates a secondary structure inversion index In1-2 based on the relationship between the vector of the secondary structure and the position of the secondary structure (compressed code of the secondary structure) in the secondary structure compressed file 53C.

[0080] Next, the preprocessing unit 151 executes a process of generating a tertiary structure compressed file 53D, and a process of generating a tertiary structure vector table VT1-3 and a tertiary structure inversion index In1-3.

[0081] The preprocessing unit 151 compares the secondary structure compressed file 53C with the tertiary structure dictionary D1-3, assigns compression codes to the array of compression codes of the secondary structures included in the secondary structure compressed file 53C on a tertiary structure basis, and generates the tertiary structure compressed file 53D.

[0082] The preprocessing unit 151 refers to the tertiary structure dictionary D1-3 to identify a secondary structure code sequence (array of compressed codes of secondary structures) corresponding to the compressed codes of the tertiary structures. The preprocessing unit 151 obtains vectors of compressed codes of each identified secondary structure from the secondary structure vector table VT1-2, and calculates vectors of compressed codes of the tertiary structures by adding each obtained vector. The preprocessing unit 151 calculates vectors of each tertiary structure by repeatedly executing the above process.

[0083] The preprocessing unit 151 generates a tertiary structure vector table VT1-3 by associating a tertiary structure (a compressed code of a tertiary structure) with a vector of the tertiary structure. The preprocessing unit 151 generates a tertiary structure inversion index In1-3 based on the relationship between the vector of the tertiary structure and the position of the tertiary structure (a compressed code of a tertiary structure) in the tertiary structure compressed file 53D.

[0084] Next, the preprocessing unit 151 executes a process of generating a higher-order structure compressed file 53E, and a process of generating higher-order structure vector tables VT1-4 and higher-order structure inversion indexes In1-4.

[0085] The preprocessing unit 151 compares the tertiary structure compressed file 53D with the higher-order structure dictionary D1-4, assigns compression codes to the array of compression codes of the tertiary structures contained in the tertiary structure compressed file 53D on a higher-order structure basis, and generates the higher-order structure compressed file 53E.

[0086] The preprocessing unit 151 refers to the higher-order structure dictionary D1-4 to identify a tertiary structure code sequence (array of compressed codes of tertiary structure) corresponding to the compressed codes of the higher-order structure. The preprocessing unit 151 obtains vectors of compressed codes of each identified tertiary structure from the tertiary structure vector table VT1-3, and calculates vectors of compressed codes of the higher-order structure by adding each obtained vector. The preprocessing unit 151 calculates vectors of each higher-order structure by repeatedly executing the above process.

[0087] The preprocessing unit 151 generates a higher-order structure vector table VT1-4 by associating a higher-order structure (compressed code of the higher-order structure) with a vector of the higher-order structure. The preprocessing unit 151 generates a higher-order structure inversion index In1-4 based on the relationship between the vector of the higher-order structure and the position of the higher-order structure (compressed code of the higher-order structure) in the higher-order structure compressed file 53E.

[0088] An example of the process in which the preprocessing unit 151 generates the alternative gene vector table T2 will be described. For example, the preprocessing unit 151 sets the tertiary structure vector contained in the secondary structure vector table VT1-2 directly to the alternative gene vector table T2. Note that when the preprocessing unit 151 receives a vector designation via the input unit 120, the preprocessing unit 151 may set the designated vector to the alternative gene genome table T2.

[0089] An example of a process in which the preprocessing unit 151 generates the learning data 65 will be described. The preprocessing unit 151 specifies the relationship between the name of the target genome and the name of the subgenome based on the genome dictionary D2. The preprocessing unit 151 specifies the vector of the target genome based on the higher-order structure dictionary D1-4 and the higher-order structure vector table VT1-4, or the tertiary structure dictionary D1-4 and the tertiary structure vector table VT1-3, and the name of the target genome. The preprocessing unit 151 specifies the vector of the subgenome based on the secondary structure dictionary D1-2 and the secondary structure vector table VT1-2, and the name of the subgenome. The preprocessing unit 151 specifies the relationship between the target genome and the subgenome through this process, and registers it in the learning data 65.

[0090] The preprocessing unit 151 repeatedly executes the above process to generate the learning data 65. Note that the information processing device 100 may obtain and use the created learning data 65 from an external device or the like.

[0091] Returning to the explanation of FIG. 7, the learning unit 152 executes learning of the learning model 70 using the learning data 65. The processing of the learning unit 152 corresponds to the processing explained in FIG. 5. The learning unit 152 acquires, from the learning data 65, a pair of a vector of a target genome (therapeutic drug) and a vector of each subgenome corresponding to this vector of the target genome. The learning unit 152 adjusts the parameters of the learning model 70 by executing learning through backpropagation so that the output value of the learning model 70 approaches the value of the vector of each subgenome when the vector of the target genome is input to the learning model 70.

[0092] The learning unit 152 executes the above process repeatedly for pairs of vectors of the target genome in the learning data 65 and vectors of each subgenome, thereby executing learning of the learning model 70.

[0093] When the calculation unit 153 receives a designation of the analysis query 80, it calculates the vector of each subgenome included in the target genome of the analysis query 80 by using the trained learning model 70. The processing of the calculation unit 153 corresponds to the processing described in Fig. 6. The calculation unit 153 may receive the analysis query 80 from the input unit 120, or may receive it from an external device via the communication unit 110.

[0094] The calculation unit 153 acquires the base sequence of the target genome included in the analysis query 80. The calculation unit 153 compares the base sequence of the target genome with the conversion table 51 to identify codons included in the base sequence of the target genome, and converts the base sequence of the target genome into a compressed code in units of codons. The calculation unit 153 also compares the codon code sequence compressed in units of codons with the protein primary structure dictionary D1-1, and converts the codon code sequence into a compressed code in units of protein primary structures.

[0095] The calculation unit 153 compares the compressed code of each converted protein primary structure with the protein primary structure vector table VT1-1 to identify the vector of the compressed code of each protein primary structure. The calculation unit 153 calculates a vector Vob80 corresponding to the target genome included in the analysis query 80 by integrating the vectors of the compressed codes of each identified protein primary structure.

[0096] In addition, when the target genome is specified by the secondary structures of multiple subgenomes, the calculation unit 153 executes the following process. The calculation unit 153 compares each secondary structure of the subgenome of the target genome with the secondary structure dictionary D1-2 and the secondary structure vector table VT1-2 to identify the vector of the secondary structure of the subgenome included in the target genome. The calculation unit 153 calculates the vector of the target genome by accumulating the vectors of the secondary structures of the identified subgenomes.

[0097] The calculation unit 153 calculates a plurality of vectors corresponding to each subgenome by inputting the vector Vob80 to the learning model 70. The calculation unit 153 outputs the calculated vector of each subgenome to the analysis unit 154. In the following description, the vector of each subgenome calculated by the calculation unit 153 is referred to as an "analysis vector." The calculation unit 153 stores the vector of each subgenome (analysis vector) in a subgenome table T1.

[0098] The analysis unit 154 searches for information on alternative gene vectors having vectors similar to the analyzed vector based on the analyzed vector. Based on the search results, the analysis unit 154 associates the vectors of each subgenome contained in the target genome with vectors of each alternative gene vector similar to the vectors (similar vectors shown below) and registers them in the alternative management table 85.

[0099] For example, the analysis unit 154 calculates the distance between the analysis vector and each vector included in the alternative gene vector table T2, and identifies a vector whose distance from the analysis vector is less than a threshold. A vector included in the alternative gene vector table T2 whose distance from the analysis vector is less than a threshold is a "similar vector." A gene vector corresponding to this similar vector is a replaceable gene vector.

[0100] The analysis unit 154 may specify the compressed code of the gene vector corresponding to the similar vector based on the secondary structure vector table VT1-2, and specify the protein primary structure included in the gene vector based on the compressed code of the specified gene vector, the secondary structure dictionary D1-2, and the protein primary structure dictionary D1-1. By executing such processing, the analysis unit 154 searches for the characteristics of the replaceable gene vector corresponding to the similar vector, and registers them in the replacement management table 85. The characteristics of the replaceable gene vector are the protein included in the gene vector and the primary structure of the protein.

[0101] The analysis unit 154 may repeatedly execute the above process for each analysis vector to search for the characteristics of gene vectors corresponding to similar vectors for each analysis vector, and register them in the substitution management table 85. The analysis unit 154 may output the substitution management table 85 to the display unit 130 for display, or may transmit it to an external device connected to the network.

[0102] Next, an example of a processing procedure of the information processing device 100 according to this embodiment will be described. Fig. 27 is a flowchart (1) showing the processing procedure of the information processing device according to this embodiment. As shown in Fig. 27, the pre-processing unit 151 of the information processing device 100 calculates a vector of the compressed code of each protein by performing Poincaré embedding (step S101).

[0103] The preprocessing unit 151 generates a compressed file table 53, a vector table 54, and an inverted index table 55 based on the base file 50, the conversion table 51, and the dictionary table 52 (step S102).

[0104] The preprocessing unit 151 generates the learning data 65 (step S103). The learning unit 152 of the information processing device 100 executes learning of the learning model 70 based on the learning data 65 (step S104).

[0105] 28 is a flowchart (2) showing the processing procedure of the information processing device according to the present embodiment. The calculation unit 153 of the information processing device 100 receives the analysis query 80 (step S201). The calculation unit 153 calculates a vector of the analysis query 80 (target genome) (step S202).

[0106] The calculation unit 153 calculates the vector of each subgenome by inputting the calculated vector of the analytical query 80 into the trained learning model 70 (step S203). The analysis unit 154 of the information processing device 100 compares the vector of each subgenome with the vector in the alternative gene vector table T2 (step S204).

[0107] The analysis unit 154 searches for replaceable gene vectors corresponding to each subgenome (step S205). The analysis unit 154 registers the search results in the replacement management table 85 (step S206).

[0108] Next, the effect of the information processing device 100 according to the present embodiment will be described. In the learning phase, the information processing device 100 executes learning of the learning model 70 based on the learning data 65 that defines the relationship between the vector of the target genome (therapeutic drug) and the vector of the subgenome. In the analysis phase, the information processing device 100 calculates the vector of each subgenome corresponding to the analysis query (target genome) by inputting the vector of the analysis query to the trained learning model 70. By using the vector of each subgenome output from the learning model 70, it is possible to easily execute detection of a replaceable gene vector similar to the subgenome contained in the target genome.

[0109] For example, when the subgenome contained in the target genome is a rare subgenome, by executing the processing of the information processing device 100, it is possible to easily search for an inexpensive gene vector that can serve as a substitute for the subgenome.

[0110] In the above-mentioned embodiment, the comparison is performed at the granularity of the subgenome (secondary structure) to search for a replaceable gene vector, but the present invention is not limited to this. For example, the information processing device 100 may perform the comparison at the granularity of a plurality of primary structures constituting the subgenome to search for a replaceable primary structure. EXAMPLES

[0111] Next, a description will be given of Example 2. Fig. 29 is a diagram for explaining an example of a learning phase process of the information processing device according to Example 2. As shown in Fig. 29, the information processing device executes learning of a learning model 91 using learning data 90. The learning model 91 corresponds to CNN, RNN, or the like.

[0112] The learning data 90 defines the relationship between the vectors of multiple subgenomes that synthesize the target genome (therapeutic drug) and the vector of a common structure that is maintained by genetic recombination based on the gene vector. For example, the vector of the subgenome corresponds to the input data, and the vector of multiple common structures is the correct answer.

[0113] The information processing device performs learning by backpropagation so that the output when the subgenome vector is input to the learning model 91 approaches the vector of each common structure. The information processing device adjusts the parameters of the learning model 91 (performs machine learning) by repeatedly performing the above process based on the relationship between the subgenome vector and the common structure vector included in the learning data 90.

[0114] Fig. 30 is a diagram for explaining the processing of the information processing device according to the second embodiment. The information processing device according to the second embodiment may have learned the learning model 90 in the same manner as the information processing device 100 according to the first embodiment. Furthermore, as described in Fig. 29, the information processing device learns a learning model 91 different from the learning model 70. The learning model 91 outputs a vector of a common structure when a vector of an analysis query (subgenome) 92 is input.

[0115] When the information processing device receives an analysis query 92 that specifies a subgenome, the information processing device converts the subgenome of the analysis query 92 into a vector Vsb92-1 using the subgenome vector table T1. The information processing device inputs the subgenome vector Vsb92-1 to the learning model 91 to calculate a vector Vcm92-1 corresponding to the common structure.

[0116] Here, the information processing device compares the subgenome vector Vsb92-1 with vectors of multiple gene vectors included in the alternative gene vector table T2. The alternative gene vector table T2 corresponds to the alternative gene vector table T2 described in the first embodiment.

[0117] The information processing device identifies a vector of a similar gene vector for the subgenome vector Vsb92-1. For example, a vector of a gene vector similar to the subgenome vector Vsb92-1 is Vt92-1. Then, it is understood that a vector of a common structure common to the subgenome of the vector Vsb92-1 and the gene vector of the vector Vt92-1 is the vector Vcm92-1 output from the learning model 91. In addition, the result of subtracting the vector Vcm92-1 of the common structure from the gene vector vector Vt92-1 is a vector of a "genetic recombination structure" that is different between the similar gene vector and the subgenome.

[0118] The information processing device registers the relationship between the vector of the common structure and the vector of the genetic recombination structure in the common structure / genetic recombination structure table 93. The information processing device generates the common structure / genetic recombination structure table 93 by repeatedly executing the above process for the vector of each subgenome.

[0119] As described above, the information processing device according to the second embodiment inputs the vector of the analysis query 92 to the trained learning model 91, and calculates vectors of each common structure corresponding to the subgenome of the analysis query. In addition, the vector of the common structure is subtracted from each vector of a gene vector similar to the subgenome, thereby calculating vectors of gene recombination structures that differ between the similar subgenome and the gene vector. By using the vectors of the common structure and the vectors of the gene recombination structure, it is possible to easily analyze better gene vectors that can be used for synthesizing and producing a target genome.

[0120] Next, an example of the configuration of an information processing device according to the present embodiment 2 will be described. Fig. 31 is a functional block diagram showing the configuration of an information processing device according to the present embodiment 2. As shown in Fig. 31, this information processing device 200 has a communication unit 210, an input unit 220, a display unit 230, a storage unit 240, and a control unit 250.

[0121] The explanations regarding the communication unit 210, the input unit 220, and the display unit 230 are similar to the explanations regarding the communication unit 110, the input unit 120, and the display unit 130 described in the first embodiment.

[0122] The storage unit 240 has a base file 50, a conversion table 51, a dictionary table 52, a compressed file table 53, a vector table 54, and an inverted index table 55. The storage unit 240 also has a subgenome table T1, an alternative gene vector table T2, a genome dictionary D2, learning data 90, a learning model 91, an analysis query 92, and a common structure / gene recombination structure table 93. The storage unit 240 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk.

[0123] The explanations regarding the base file 50, the conversion table 51, the dictionary table 52, the compressed file table 53, the vector table 54, the transposition index table 55, the subgenome table T1, the alternative gene vector table T2, and the genome dictionary D2 are the same as those described in the first embodiment. The learning data 90 is the same as those described in Fig. 29. The explanations regarding the learning model 91 and the analysis query 92 are the same as those described in Fig. 30.

[0124] The common structure / genetic recombination structure table 93 includes information on gene recombination structure vectors for recombining a gene vector similar to the common structure vector into a subgenome, as described in Fig. 30. In Fig. 30, for example, the common structure / genetic recombination structure table 93 includes a gene recombination structure vector corresponding to Vcm92-1. The vector obtained by multiplying the common structure vector and the gene recombination structure vector becomes the vector corresponding to the gene vector vector.

[0125] Returning to Fig. 31, the control unit 250 has a preprocessing unit 251, a learning unit 252, a calculation unit 253, and an analysis unit 254. The control unit 250 is realized by, for example, a CPU or an MPU. The control unit 250 may also be executed by, for example, an integrated circuit such as an ASIC or an FPGA.

[0126] The explanation of the preprocessing unit 251 is the same as the explanation of the processing of the preprocessing unit 151 described in the embodiment 1. The preprocessing unit 251 generates a base file 50, a conversion table 51, a dictionary table 52, a compressed file table 53, a vector table 54, a transposition index table 55, a subgenome table T1, and an alternative gene vector table T2. The preprocessing unit 251 may obtain the learning data 90 from an external device, or the preprocessing unit 251 may generate the learning data 90.

[0127] When the calculation unit 253 receives a designation of the analysis query 92, the calculation unit 253 uses the trained learning model 91 to calculate vectors of each common structure to be genetically recombined in the synthesis pathway of the subgenome of the analysis query 92. The calculation unit 253 outputs the calculated vectors of each common structure to the analysis unit 254.

[0128] In the following description, each common structure vector calculated by the calculation unit 253 will be referred to as a "common structure vector."

[0129] The analysis unit 254 generates a common structure / gene recombination mechanism table 93 based on the subgenome vector, the common structure vector, and the gene vector table T2 of the analysis query 92. An example of the processing of the analysis unit 254 will be described below.

[0130] The analysis unit 254 calculates the distance between the subgenome vector and each vector included in the alternative gene vector table T2, and identifies vectors whose distance to the subgenome vector is less than a threshold. A vector included in the alternative gene vector table T2 whose distance to the subgenome vector is less than a threshold is referred to as a "similar vector."

[0131] The analysis unit 254 calculates a vector of a genetic recombination structure by subtracting the common structure vector from the similar vector, and specifies a correspondence relationship between the common structure vector and the vector of the genetic recombination structure. The analysis unit 254 registers the common structure vector and the vector of the genetic recombination structure in a common structure / genetic recombination structure table 93. The analysis unit 245 generates the common structure / genetic recombination structure table 93 by repeatedly executing the above process. The analysis unit 245 may output the common structure / genetic recombination structure table 93 to the display unit 230 for display, or may transmit it to an external device connected to a network.

[0132] Next, a description will be given of an example of a processing procedure of the information processing device 200 according to the embodiment 2. Fig. 32 is a flowchart showing the processing procedure of the information processing device according to the embodiment 2. The calculation unit 253 of the information processing device 200 receives the analysis query 92 (step S301).

[0133] The calculation unit 253 converts the subgenome of the analysis query 92 into a vector based on the subgenome table T1 (step S302).

[0134] The calculation unit 253 calculates a vector of a common structure by inputting the vector of the subgenome to the trained learning model 91 (step S303). The analysis unit 254 of the information processing device 200 identifies a similar vector based on the distance between the vector of the common structure and each vector in the alternative gene vector table T2 (step S304).

[0135] The analysis unit 254 calculates the vector of the genetic recombination structure by subtracting the vector of the common structure from each vector of the genetic vector similar to the subgenome (step S305). The analysis unit 254 registers the relationship between the vector of the common structure and the vector of the genetic recombination structure in the common structure / genetic recombination structure table 93 (step S306). The analysis unit 254 outputs information of the common structure / genetic recombination structure table (step S307).

[0136] Next, the effect of the information processing device 200 according to the second embodiment will be described. The information processing device 100 inputs a vector of the analysis query 92 to the trained learning model 91, and calculates vectors of each common structure corresponding to the subgenome of the analysis query. In addition, by subtracting vectors of each common structure from vectors of gene vectors similar to the subgenome, vectors of gene recombination structures that differ between the similar subgenome and the gene vector are calculated. By using the above-mentioned vectors of common structures and vectors of gene recombination structures, it is possible to easily analyze better gene vectors that can be used for gene recombination, resynthesis, and production into a target genome.

[0137] Subgenomes and gene vectors are secondary structures composed of multiple protein primary structures. In addition, by using the variance vector of the protein primary structure, it is possible to estimate the protein primary structure adjacent to a certain protein primary structure, and this can be applied to the evaluation of the degree of binding and stability of each protein primary structure. With regard to the proven genetic recombination from gene vectors to subgenomes, machine learning can be performed based on the variance vectors of multiple protein secondary structures that constitute the secondary structures of subgenomes and gene vectors, thereby improving the analytical accuracy of diversion from gene vectors, genetic recombination, and resynthesis.

[0138] Next, an example of a hardware configuration of a computer that realizes the same functions as the information processing device 100 (200) shown in the above embodiment will be described. Fig. 33 is a diagram showing an example of a hardware configuration of a computer that realizes the same functions as the information processing device of the embodiment.

[0139] 9, computer 300 has CPU 301 for executing various arithmetic processes, input device 302 for receiving data input from a user, and display 303. Computer 300 also has communication device 304 for transmitting and receiving data to and from external devices etc. via a wired or wireless network, and interface device 305. Computer 300 also has RAM 306 for temporarily storing various information, and hard disk device 307. Each of devices 301 to 307 is connected to bus 308.

[0140] The hard disk device 307 stores a pre-processing program 307a, a learning program 307b, a calculation program 307c, and an analysis program 307d. The CPU 301 reads out each of the programs 307a to 307d and loads them in the RAM 306.

[0141] The pre-processing program 307a functions as a pre-processing process 306a. The learning program 307b functions as a learning process 306b. The calculation program 307c functions as a calculation process 306c. The analysis program 307d functions as an analysis process 306d.

[0142] The preprocessing process 306a corresponds to the processing by the preprocessing units 151 and 251. The learning process 306b corresponds to the processing by the learning units 152 and 252. The calculation process 306c corresponds to the processing by the calculation units 153 and 253. The analysis process 306d corresponds to the processing by the analysis unit 154.

[0143] It is not necessary that each of the programs 307a to 307d is stored in the hard disk device 307 from the beginning. For example, each program may be stored in a "portable physical medium" such as a flexible disk (FD), CD-ROM, DVD, magneto-optical disk, or IC card that is inserted into the computer 300. Then, the computer 300 may read and execute each of the programs 307a to 307d. [Explanation of symbols]

[0144] 110,210 Communications Department 120,220 Input section 130,230 Display section 140,240 storage section 150,250 Control section 151,251 Pretreatment section 152,252 Learning Department 153,253 Calculation section 154,254 Analysis Department

Claims

1. On the computer, A learning process is performed based on learning data in which a vector corresponding to a genome is used as input data and vectors corresponding to a plurality of subgenomes constituting the genome are used as correct values, the learning process adjusting parameters of the learning model so that an output obtained when the input data is input to the learning model approaches the correct value; When a genome to be analyzed is received, the genome to be analyzed is input to the learning model, thereby calculating vectors of a plurality of subgenomes corresponding to the genome to be analyzed. An information processing program that causes a process to be executed.

2. The information processing program according to claim 1, further comprising causing the computer to execute a process of searching for alternative gene vectors that can be substituted for the subgenomes based on the degree of similarity between the calculated vectors of the multiple subgenomes and the vectors of multiple alternative gene vectors that are alternative candidates.

3. The information processing program according to claim 1, further comprising causing a computer to execute a process of calculating a vector of the genome to be analyzed by integrating vectors of the multiple secondary structures contained in the genome to be analyzed, the process including:

4. On the computer, A learning process is performed in which vectors of a plurality of subgenomes included in a synthetic route for producing a genome are used as input data, and a vector of a common structure showing a common structure among the subgenome structures and the gene vector structures is used as a correct value, based on which the parameters of the learning model are adjusted so that an output when the input data is input to the learning model approaches the correct value; When an input of a subgenome to be analyzed is received, a vector of the subgenome to be analyzed is input to the learning model, thereby calculating a vector of a common structure corresponding to the subgenome to be analyzed. An information processing program that causes a process to be executed.

5. 1. A computer-implemented information processing method, comprising: A learning process is performed based on learning data in which a vector corresponding to a genome is used as input data and vectors corresponding to a plurality of subgenomes constituting the genome are used as correct values, the learning process adjusting parameters of the learning model so that an output obtained when the input data is input to the learning model approaches the correct value; When a genome to be analyzed is received, the genome to be analyzed is input to the learning model to calculate vectors of a plurality of sub-vectors corresponding to the gene vector to be analyzed.

2. An information processing method comprising:

6. 1. A computer-implemented information processing method, comprising: A learning process is performed in which vectors of a plurality of subgenomes included in a synthetic route for producing a genome are used as input data, and a vector of a common structure showing a common structure among the subgenome structures and the gene vector structures is used as a correct value, based on which the parameters of the learning model are adjusted so that an output when the input data is input to the learning model approaches the correct value; When a subgenome to be analyzed is received, a vector of the subgenome to be analyzed is input to the learning model, thereby calculating a vector of a common structure corresponding to the subgenome to be analyzed.

2. An information processing method comprising:

7. a learning unit that performs learning based on learning data in which a vector corresponding to a genome is used as input data and vectors corresponding to a plurality of subgenomes constituting the genome are used as correct values, and adjusts parameters of the learning model so that an output when the input data is input to the learning model approaches the correct value; a calculation unit that, when receiving a genome to be analyzed, calculates vectors of a plurality of subgenomes corresponding to the genome to be analyzed by inputting the genome to be analyzed into the learning model; 13. An information processing device comprising:

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