Traditional Chinese medicine charm selection method and device

Abstract

The invention relates to the technical field of traditional Chinese medicine development, in particular to a traditional Chinese medicine charm selection method and device, and the method comprises the steps: determining a background pathway, a traditional Chinese medicine set and patient data; determining a drug disorder pathway based on the background pathway and the traditional Chinese medicine set, and determining a disease disorder pathway based on the background pathway and the patient data; determining a molecular herbal database based on the disease disorder pathway and the drug disorder pathway; determining an initial group based on the molecular herbal database; wherein the initial group comprises a plurality of individuals, and the individuals comprise charms; and determining a fitness function, performing a genetic algorithm based on the initial population and the fitness function, and determining a target charm. Based on related cell line or carrier gene expression profile data before and after traditional Chinese medicine intervention, according to an enrichment analysis result of differential genes before and after traditional Chinese medicine intervention, a novel traditional Chinese medicine prescription capable of reversing disease-related disorder pathways is searched, so that the time required for searching an optimal solution is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of traditional Chinese medicine development, and in particular to a method for selecting a good traditional Chinese medicine prescription. Background Art

[0002] Current methods for formulating traditional Chinese medicine include exhaustive method and TCMFP.

[0003] The exhaustive method is based on a pre-set TCM drug pool and enumerates all TCM compatibility combinations to find the best prescription. Although this method can ensure the comprehensive exploration and capture of all potential beneficial formulas and ensure the comprehensiveness of the screening conclusion, it is extremely time-consuming when the number of good prescriptions is large. In addition, the exhaustive method itself does not support the direct optimization of drug dosage configuration, which is another major limitation in its practicality.

[0004] TCMFP is a prediction method for good Chinese medicine prescriptions (TCMFP) that integrates the experience of good Chinese medicine prescriptions, modern pharmacology, network pharmacology and genetic algorithms. This method uses disease-related genes, protein-protein interactions (PPIs) and Chinese medicine components to construct a "target-molecule-TCM" network, establish a TCM database, and collect drug pairs in classic prescriptions from databases, ancient books or literature, and organize the data to form a TCM pair dataset. Hscore is calculated based on the TCM dataset and the "target-molecule-TCM" network, and the random walk algorithm is used to screen out important TCMs. Combined with the TCM drug pair dataset, Pscore is calculated to determine the high-frequency TCM combination, and finally Hscore and Pscore are integrated to calculate FmapScore as the evaluation score of the herbal formula. Genetic algorithms are used to predict the best prescriptions, including prescription initialization, calculation of FmapScore, retention of the top 50% of high-scoring prescriptions, and crossover mutation. Then, the predicted TCM prescriptions are analyzed for effectiveness. Finally, based on the protein interaction network, KEGG pathway and GO enrichment analysis, the predicted best herbal formula is evaluated for its impact on the disease target.

[0005] In summary, the exhaustive method searches for good prescriptions by enumerating all the combinations of Chinese medicines, but it is computationally intensive, time-consuming, and cannot achieve the quantitative ratio of good prescriptions. The TCMFP method analyzes information from existing databases such as gene network libraries, protein network libraries, disease network libraries, drug network libraries, and classic prescription databases. It has the disadvantages of large consumption of computing resources and reliance on prior database information. It is often limited by established experience and knowledge frameworks, thus limiting the possibility of discovering new and effective prescriptions. Therefore, a better solution is urgently needed. Summary of the invention

[0006] In view of this, the present invention provides a method for selecting a good Chinese medicine prescription. One or more embodiments of this specification also relate to a Chinese medicine prescription selection device, a computing device, a computer-readable storage medium and a computer program to solve the technical defects existing in the prior art.

[0007] According to a first aspect of the present invention, a method for selecting a good Chinese medicine prescription is provided, comprising:

[0008] Identify background pathways, TCM collections, and patient data;

[0009] Identify drug-dysregulation pathways based on background pathways and TCM collections, and identify disease-dysregulation pathways based on background pathways and patient data;

[0010] Identify molecular herbal databases based on disease- and drug-disordered pathways;

[0011] Determine an initial group based on a molecular herbal database; wherein the initial group includes multiple individuals, and the individuals include Shan Fang;

[0012] Determine the fitness function, perform genetic algorithm based on the initial population and the fitness function, and determine the target solution.

[0013] In a possible implementation, determining a drug disorder pathway based on a background pathway and a set of traditional Chinese medicines includes:

[0014] Determine drug expression profile data by intervening cells with a collection of traditional Chinese medicines;

[0015] Gene enrichment analysis based on drug expression profile data and background pathways was performed to identify drug dysregulation pathways.

[0016] In a possible implementation, determining a drug disorder pathway based on a background pathway and a set of traditional Chinese medicines includes:

[0017] Perform differential gene analysis based on patient data to determine disease expression profile data;

[0018] Identify disease dysregulated pathways based on disease expression profiling data and background pathways.

[0019] In a possible implementation, determining an initial population based on a molecular herbal database includes:

[0020] Determine the number of good parties, and determine the group size parameter based on the number of good parties;

[0021] Determine the number of compatible Chinese medicines, and determine the individual length parameters based on the number of compatible Chinese medicines;

[0022] The initial population is determined based on the population size parameter and the individual length parameter.

[0023] In a possible implementation, a genetic algorithm is performed based on an initial population and a fitness function to determine a target method, including:

[0024] Determine the good and bad recipes based on the initial population and fitness function;

[0025] Reduce the crossover probability and mutation probability of good and good parties, and increase the crossover probability and mutation probability of bad and good parties;

[0026] Based on the changed crossover probability and mutation probability, a genetic algorithm is used to determine the target solution.

[0027] In a possible implementation, a genetic algorithm is performed based on an initial population and a fitness function to determine a target method, including:

[0028] Determine a target generation number, and after performing a genetic algorithm to the target generation number based on an initial population and a fitness function, determine at least two sub-evolution processes;

[0029] A genetic algorithm is performed in parallel based on at least two sub-evolution processes to determine a target solution.

[0030] In a possible implementation, the initial group includes a first individual and a second individual;

[0031] The compatibility scheme of the first individual and the second individual is the same; wherein the compatibility scheme includes at least two Chinese medicines, and the at least two Chinese medicines include the target Chinese medicine;

[0032] The concentration of the target Chinese medicine in the first individual is a first concentration;

[0033] The concentration of the target Chinese medicine in the second individual is a second concentration;

[0034] The first concentration and the second concentration are different.

[0035] According to a second aspect of the present invention, there is provided a device for selecting a good Chinese medicine prescription, comprising:

[0036] A data determination module configured to determine background pathways, a set of traditional Chinese medicines, and patient data;

[0037] a pathway determination module configured to determine a drug disorder pathway based on a background pathway and a set of traditional Chinese medicines, and to determine a disease disorder pathway based on the background pathway and patient data;

[0038] a database determination module configured to determine a molecular herbal database based on a disease disorder pathway and a drug disorder pathway;

[0039] A group determination module is configured to determine an initial group based on a molecular herbal database; wherein the initial group includes a plurality of individuals, and the individuals include a good prescription;

[0040] The good party selection module is configured to determine a fitness function, and to perform a genetic algorithm based on an initial population and the fitness function to determine a target good party.

[0041] According to a third aspect of the present invention, there is provided a computing device, comprising:

[0042] Memory and processor;

[0043] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the above-mentioned method for selecting a good Chinese medicine prescription are implemented.

[0044] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned method for selecting a good Chinese medicine prescription.

[0045] According to a fifth aspect of the present invention, a computer program is provided, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned method for selecting a good Chinese medicine prescription.

[0046] The present invention provides a method and device for selecting a good Chinese medicine prescription, wherein the method comprises: determining a background pathway, a Chinese medicine set and patient data; determining a drug disorder pathway based on the background pathway and the Chinese medicine set, and determining a disease disorder pathway based on the background pathway and the patient data; determining a molecular herbal database based on the disease disorder pathway and the drug disorder pathway; determining an initial population based on the molecular herbal database; wherein the initial population includes a plurality of individuals, and the individuals include a good prescription; determining a fitness function, and performing a genetic algorithm based on the initial population and the fitness function to determine a target good prescription. Based on the gene expression spectrum data of relevant cell lines or model animals before and after Chinese medicine intervention, and according to the enrichment analysis results of differential genes before and after Chinese medicine intervention, a new Chinese medicine good prescription that can reverse the disease-related disorder pathway is found, thereby shortening the time required to find the optimal solution and improving the therapeutic effect of the good prescription. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of a method for selecting a good Chinese medicine prescription provided by an embodiment of this specification;

[0048] Figure 2 It is a schematic diagram of a framework of a method for selecting a good Chinese medicine prescription provided in one embodiment of this specification;

[0049] Figure 3 It is a genetic algorithm principle diagram of a method for selecting a good Chinese medicine prescription provided by an embodiment of this specification;

[0050] Figure 4This is a schematic diagram of the change in reversal rate of a method for selecting a good Chinese medicine prescription provided in one embodiment of this specification;

[0051] Figure 5 It is a schematic diagram of the fluctuation of the reversal rate of a method for selecting a good Chinese medicine prescription provided in one embodiment of the present specification;

[0052] Figure 6 This is a schematic diagram of the reversal rate distribution of a method for selecting a good Chinese medicine prescription provided in one embodiment of this specification;

[0053] Figure 7 It is a schematic diagram of the structure of a device for selecting a good Chinese medicine prescription provided by one embodiment of this specification;

[0054] Figure 8 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0055] Many specific details are described in the following description to facilitate a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of this specification, so this specification is not limited to the specific implementation disclosed below.

[0056] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0057] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0058] First, the terms involved in one or more embodiments of this specification are explained.

[0059] Gene Set Enrichment Analysis (GSEA): is a statistical method widely used in bioinformatics research, mainly used to test the enrichment of certain functions or features in a gene set. The main purpose of enrichment analysis is to find biologically meaningful patterns and functions from a large amount of genetic data.

[0060] NGS (Next Generation Sequencing): also known as high-throughput sequencing, is characterized by high output and high resolution. It can read the sequences of hundreds of thousands to millions of DNA molecules in parallel at one time. While providing rich genetic information, it can also greatly reduce sequencing costs and shorten sequencing time.

[0061] HTS 2 :high throughput sequencing-based high throughput screening, high throughput drug screening based on high throughput sequencing.

[0062] Pathway: A model that describes the interactions among genes, proteins or metabolites within cells, tissues or individuals.

[0063] FDR: false discovery rate, false discovery rate: in transcriptome analysis, it is mainly used in the analysis of differentially expressed genes to control the proportion of false positive results in the final analysis results.

[0064] In this specification, a method for selecting a good Chinese medicine prescription is provided. This specification also relates to a device for selecting a good Chinese medicine prescription, a computing device, and a computer-readable storage medium, which are described in detail one by one in the following embodiments.

[0065] See also Figure 1 , Figure 1 A flow chart of a method for selecting a good Chinese medicine prescription provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0066] Step 101: Determine background pathways, TCM collections, and patient data.

[0067] Among them, the background pathway can be a pathway related to the disease; the Chinese medicine set can be a set of all selected Chinese medicines used to form a good prescription; the patient data can be the gene expression spectrum data of diseased patients and healthy people; the good prescription can be a combination of Chinese medicines or foods, which can also be called a prescription.

[0068] In practical applications, HTS 2, NGS or gene chip platform to obtain the characteristic gene expression profile of each cell after TCM treatment and perform differential gene analysis to obtain drug expression profile data. Furthermore, disease-related databases and literature are searched to collect pathways as background pathways, and gene expression profile data of disease patients and healthy people can be downloaded from published public databases as patient data.

[0069] The basic data of the embodiments of this specification are accurate, and all of these basic data are obtained through experiments rather than relying on additional inferences, thereby ensuring the accuracy of the data.

[0070] Step 102: Determine a drug disorder pathway based on the background pathway and the Chinese medicine set, and determine a disease disorder pathway based on the background pathway and the patient data.

[0071] Among them, the significantly enriched pathways were screened out according to the FDR threshold and were called dysregulated pathways.

[0072] In a possible implementation, a drug disorder pathway is determined based on a background pathway and a Chinese medicine collection, including: intervening in cells through a Chinese medicine collection to determine drug expression profile data; and performing gene enrichment analysis based on the drug expression profile data and the background pathway to determine the drug disorder pathway.

[0073] In practical applications, see Figure 2 ,Based on the background pathways screened in the above steps, gene enrichment analysis of drug expression profile data was performed to obtain drug disorder pathways, namely Figure 2 GSEA analysis outputs the standardized enrichment score (NES value) and significance test value (FDR value) of each Chinese medicine on the background pathway. When the FDR value is less than the set FDR threshold, the original NES is retained. When the FDR value exceeds the set threshold, the NES value is reset to 0. The NES value can be regarded as a quantitative value of the effect on the pathway.

[0074] In a possible implementation, determining a drug disorder pathway based on background pathways and a collection of traditional Chinese medicines includes: determining disease expression profile data by performing differential gene analysis based on patient data; and determining a disease disorder pathway based on the disease expression profile data and background pathways.

[0075] In practical applications, see Figure 2 , based on the gene expression profile data of disease patients and healthy people, differential gene analysis is performed, and then Gene Set Enrichment Analysis (GSEA) is performed based on the background pathways screened out in the above steps. The significantly enriched pathways are screened out according to the FDR threshold to obtain the disease disorder pathway, that is, Figure 2 The spectrum of dysregulated pathways in .

[0076] The embodiments of this specification combine drug gene expression data to improve the scientificity and pertinence of Chinese medicine prescriptions through scientific analysis, making the drug effects more accurate and effective. And with the reversal of disease signal pathways as the goal orientation, it effectively guides the screening and optimization process of Chinese medicine prescriptions and enhances the pertinence and accuracy of drug action mechanisms.

[0077] Step 103: Determine a molecular herbal database based on the disease disorder pathway and the drug disorder pathway.

[0078] In practical applications, after the above steps, a molecular herbal database can be established based on the obtained disease disorder pathways and drug disorder pathways, so as to select a good prescription based on the molecular herbal database. See the following steps for details.

[0079] Step 104: Determine an initial group based on the molecular herbal database; wherein the initial group includes multiple individuals, and the individuals include good prescriptions.

[0080] In one possible implementation, determining an initial population based on a molecular herbal database includes: determining the number of good prescriptions, and determining a population size parameter based on the number of good prescriptions; determining the number of compatible Chinese medicines, and determining an individual length parameter based on the number of compatible Chinese medicines; and determining the initial population based on the population size parameter and the individual length parameter.

[0081] The number of good prescriptions can be the number of good prescriptions to be selected, and the number of compatible Chinese medicines can be the number of types of Chinese medicines in each good prescription.

[0082] In practical applications, see Figure 3 The genetic algorithm simulates the selection, crossover and mutation mechanisms in natural evolution, and then uses the disease molecular signaling pathways obtained from the analysis of clinical patient omics data as targets, combined with the drug cell gene expression profile data, to screen and optimize the Chinese medicine formula with the reversal of the disease signaling pathway ratio as the core.

[0083] Specifically, the population needs to be initialized first. The population size and individual length need to be determined. The population size is the number of randomly generated prescriptions, which is an integer, denoted by N; the individual length is the number of compatible Chinese medicines contained in the generated prescription, which is an integer, denoted by L.

[0084] Step 105: Determine the fitness function, perform a genetic algorithm based on the initial population and the fitness function, and determine the target solution.

[0085] The target good prescription may be the good prescription finally screened out.

[0086] In practical applications, the embodiments of this specification introduce the concepts of adaptive crossover probability, adaptive mutation probability, elite retention strategy and parallel directed evolution on the basis of traditional genetic algorithms. With the help of adaptive strategies, the convergence of the algorithm is guaranteed while the population maintains diversity. The elite retention strategy ensures that in the process of iterative evolution, excellent prescriptions can be retained and enter the next generation by setting a minimum reversal rate threshold, so that the whole can continue to evolve in the most optimized direction. This method can not only ensure that a certain number of high-efficacy prescriptions are screened, but also improve computing efficiency and shorten computing time. Parallel directed evolution can expand the search range, protect the diversity of combination results, and speed up the speed of formula formation. See below for details.

[0087] In a possible implementation, a genetic algorithm is performed based on an initial population and a fitness function to determine a target good recipe, including: determining a good good recipe and an inferior good recipe based on the initial population and the fitness function; reducing the crossover probability and mutation probability of the good good recipe, and increasing the crossover probability and mutation probability of the inferior good recipe; and performing a genetic algorithm based on the changed crossover probability and mutation probability to determine the target good recipe.

[0088] In practical applications, after determining the initial population, it is necessary to determine the fitness function, and the value of the fitness function is calculated for each group. The fitness function calculation will be performed for the first time in the initial group. After that, when applying genetic operators such as selection, crossover and mutation, the same calculation will be performed for each new generation of groups.

[0089] Specifically, fitness is defined as:

[0090]

[0091] Among them, m means there are m disordered pathways, Cnes i Indicates the NES value of Chinese medicine on the ith disorder pathway, Dnes i Represents the NES value of the patient on the i-th disordered pathway.

[0092] Furthermore, the selection in the genetic algorithm is performed by a sampling method, preferably, a roulette algorithm can be used. The basic idea of ​​the algorithm is that the probability of each individual being selected is proportional to its fitness, that is, the greater the fitness, the greater the probability of being selected, and vice versa. Its mathematical expression is as follows:

[0093]

[0094] Where N represents the number of formulas, f(x i ) represents the fitness of the ith group, and the denominator represents the sum of the fitness of all individuals.

[0095] In order to ensure that excellent recipes can be retained to the next generation more easily, an elite retention strategy (i.e., minimum reversal rate) is introduced. In the genetic algorithm, if the reversal rate of a recipe is higher than this minimum reversal rate, then the recipe will be directly retained to the next generation without the need for selection (roulette). This helps protect excellent recipes from being eliminated prematurely, thereby improving the algorithm's search efficiency and performance.

[0096] Furthermore, in order to make the genetic algorithm more suitable for finding good solutions, an adaptive crossover strategy is introduced. The goals of this strategy are twofold:

[0097] 1. For the excellent prescriptions that have been identified by fitness, they should be retained as much as possible and the crossover probability should be reduced; on the contrary, for the inferior prescriptions, the crossover probability should be increased;

[0098] 2. In the iteration close to convergence, the high crossover probability consistent with the previous one is no longer needed. On the contrary, the later iterations should try to control the range of crossover so that the population can converge quickly after finding the optimal formula.

[0099] Adaptive crossover is defined as:

[0100]

[0101] Among them, P min and P max is the minimum and maximum value of the crossover probability, f max is the maximum fitness of the population, f avg is the average fitness of the population, and f is the larger fitness of the two groups participating in the crossover operation.

[0102] Further: Adaptive mutation. The idea is the same as the adaptive crossover in the above steps.

[0103] Adaptive mutation is defined as:

[0104]

[0105] Among them, P min and P max is the minimum and maximum value of the mutation probability, f max is the maximum fitness of the population, f avg is the average fitness of the population, and f is the larger fitness of the two groups involved in the mutation operation.

[0106] The embodiments of this specification introduce adaptive strategies and elite retention strategies to ensure the long-term diversity and evolutionary ability of the population. The adaptive strategy allows the algorithm to self-regulate according to the situation and adapt to problems of different complexity, thereby improving the flexibility of the algorithm.

[0107] Furthermore, in genetic algorithms, the number of iterations determines the total number of rounds of the algorithm, that is, the number of times the population evolves. The number of generations of evolution needs to be reasonably set according to the characteristics and requirements of the problem.

[0108] The setting of the evolutionary algebra in the embodiment of this specification is flexible and adjustable, and the number of algorithm iterations can be dynamically adjusted according to the characteristics of the problem, ensuring that the algorithm is neither premature nor over-searching, thereby improving the adaptability and robustness of the algorithm.

[0109] In one possible implementation, a genetic algorithm is performed based on an initial population and a fitness function to determine a target solution, including: determining a target generation, after performing the genetic algorithm based on the initial population and the fitness function to the target generation, determining at least two sub-evolution processes; and performing the genetic algorithm in parallel based on the at least two sub-evolution processes to determine the target solution.

[0110] In practical applications, see Figure 4 As the number of evolutionary generations increases, some characteristics (such as reversal rate) may not change significantly even after multiple iterations and selections, which may cause the algorithm to fall into a local optimal solution, limiting the diversity and innovation of the results. The present invention proposes a parallel directed evolution technology to overcome the above limitations by processing multiple independent evolutionary processes in parallel.

[0111] The embodiments of this specification introduce genetic algorithms and parallel directed evolution methods to accelerate the screening process of traditional Chinese medicine prescriptions while ensuring comprehensiveness and accuracy, greatly improving efficiency.

[0112] In one embodiment, 300 single drugs are combined with 6 and 7 drugs. Exhaustive method and genetic algorithm are used for formula combination and good prescription screening, both of which have 100 threads, and the running results are as follows:

[0113] Table 1

[0114]

[0115] From the data in the above table, we can observe that, under the premise of maintaining the maximum reversal rate, the running time of the genetic algorithm is less than that of the exhaustive method, and the speed increase is even greater as the number of medicinal flavors increases.

[0116] In another embodiment, 300 single drugs are combined with 6 and 7 drugs. Adaptive genetic algorithm and non-adaptive genetic algorithm are used for formula combination and good prescription screening, both of which have 100 threads, and the running results are as follows:

[0117] Table 2

[0118]

[0119] In this patent, the optimal result ratio refers to the ratio of the number of formulas that can achieve the highest reversal rate in the genetic algorithm output formula to the number of all formulas with the highest reversal rate found by the exhaustive method.

[0120] As can be seen from the above table, in the results, the adaptive method performs better than the non-adaptive method in terms of the ability to obtain the optimal solution of the prescription.

[0121] For further information, see Figure 5 , Figure 5 The figure shows the fluctuation of the maximum reversal rate obtained by the non-adaptive and adaptive methods when different minimum reversal rate thresholds are set. Genetic algorithm is a heuristic optimization algorithm, which involves random factors. From the above chart, it can be seen that when different minimum reversal rates are set, the fluctuation of the maximum reversal rate obtained by the non-adaptive method is more significant than that of the adaptive method in most cases, and its average value is also relatively low. Figure 6 ,from Figure 6 The data distribution shows that the adaptive results are significantly better than the non-adaptive results, whether considering the number of top results with the highest reversal rate or the overall number of all results. This shows that the adaptive method performs better in the screening and optimization process.

[0122] In another embodiment, 300 single drugs are combined with 6 and 7 drugs. The random mutation mode and the directed mutation mode are used for formula combination and good prescription screening, both of which have 100 threads, and the running results are as follows:

[0123] Table 3

[0124]

[0125] As can be seen from the above table, the directed mutation mode has shown a better ability than the random mutation mode in obtaining the optimal solution for the formula.

[0126] In the process of prescription screening, the embodiments of this specification use fitness function to screen prescriptions, which is a key indicator to measure whether a candidate prescription is close to the ideal state. The fitness screening method is based on the principle of evolutionary computation, and by simulating the process of natural selection and genetic variation, it efficiently selects the solution with the best comprehensive performance from a large number of candidate prescriptions. This method not only greatly improves the screening efficiency, but also ensures the high quality and practicality of the screening results.

[0127] In one possible implementation, the initial group includes a first individual and a second individual; the compatibility scheme of the first individual and the second individual is the same; wherein the compatibility scheme includes at least two Chinese medicines, and the at least two Chinese medicines include a target Chinese medicine; the concentration of the target Chinese medicine in the first individual is a first concentration; the concentration of the target Chinese medicine in the second individual is a second concentration; the first concentration and the second concentration are different.

[0128] In practical applications, in the process of prescription compatibility, three different concentration gradients are set for each single drug in the drug pool. The genetic algorithm first randomly generates a set of initial compatibility schemes, in which different concentrations of the same single drug will not appear repeatedly in the same compatibility, thus forming an initial population. Next, the algorithm calculates the fitness value of each compatibility scheme according to the preset evaluation criteria (pathway reversal rate). This fitness value will be used as an important indicator to measure the quality of the compatibility scheme. Then, the algorithm will sort the compatibility schemes in the population according to the fitness value, and apply the elite retention strategy to select excellent individuals with higher fitness to enter the next generation directly. This strategy ensures the continuation of excellent genes in the evolution process. Subsequently, the algorithm will perform crossover and mutation operations on the selected individuals. The crossover operation simulates the gene recombination in biological evolution, and generates new compatibility schemes by exchanging some single drugs of different individuals. The mutation operation simulates the gene mutation in biological evolution, randomly changing some gene values ​​in individuals to increase the diversity of the population. Finally, in order to ensure the effectiveness of the compatibility scheme, the algorithm will perform deduplication operations on the newly generated compatibility scheme. If different concentrations of the same drug appear in the same combination at the same time, the algorithm will replace the remaining drugs with other different drugs not included in the combination while retaining one of the repeated drugs, to ensure the rationality and effectiveness of the combination scheme.

[0129] Specifically, 13 single drugs, 3 concentrations, and 2-8 drugs were combined. The exhaustive method was used to formulate the single concentration drugs, and the algorithm of this scheme was used to formulate the mixed concentrations and screen the best prescriptions. The results of screening the best prescriptions with the best reversal rate are as follows:

[0130] Table 4

[0131]

[0132] From the data in the above table, we can observe that when screening good prescriptions, considering the concentration-dose ratio of the prescription can help screen out better prescriptions.

[0133] The embodiments of this specification provide a method and device for selecting a good Chinese medicine prescription, wherein the method for selecting a good Chinese medicine prescription includes: determining background pathways, Chinese medicine collections, and patient data; determining drug disorder pathways based on background pathways and Chinese medicine collections, and determining disease disorder pathways based on background pathways and patient data; determining a molecular herbal database based on disease disorder pathways and drug disorder pathways; determining an initial population based on the molecular herbal database; wherein the initial population includes multiple individuals, and the individuals include a good prescription; determining a fitness function, and performing a genetic algorithm based on the initial population and the fitness function to determine a target good prescription. Based on the gene expression spectrum data of relevant cell lines or model animals before and after Chinese medicine intervention, and according to the enrichment analysis results of differential genes before and after Chinese medicine intervention, a new Chinese medicine prescription that can reverse disease-related disorder pathways is found, thereby shortening the time required to find the optimal solution.

[0134] Corresponding to the above method embodiment, this specification also provides an embodiment of a device for selecting a good Chinese medicine prescription. Figure 7 FIG. 1 is a schematic diagram showing a structure of a Chinese medicine prescription selection device provided by an embodiment of the present specification. Figure 7 As shown, the device comprises:

[0135] The data determination module 701 is configured to determine background pathways, Chinese medicine sets, and patient data;

[0136] a pathway determination module 702 configured to determine a drug disorder pathway based on background pathways and a set of Chinese medicines, and to determine a disease disorder pathway based on background pathways and patient data;

[0137] A database determination module 703 is configured to determine a molecular herbal database based on disease disorder pathways and drug disorder pathways;

[0138] The group determination module 704 is configured to determine an initial group based on the molecular herbal database; wherein the initial group includes a plurality of individuals, and the individuals include a good prescription;

[0139] The good party selection module 705 is configured to determine a fitness function, and perform a genetic algorithm based on the initial population and the fitness function to determine a target good party.

[0140] In a possible implementation, the path determination module 702 is further configured to:

[0141] Determine drug expression profile data by intervening cells with a collection of traditional Chinese medicines;

[0142] Gene enrichment analysis based on drug expression profile data and background pathways was performed to identify drug dysregulation pathways.

[0143] In a possible implementation, the path determination module 702 is further configured to:

[0144] Perform differential gene analysis based on patient data to determine disease expression profile data;

[0145] Identify disease dysregulated pathways based on disease expression profiling data and background pathways.

[0146] In a possible implementation, the group determination module 704 is further configured to:

[0147] Determine the number of good parties, and determine the group size parameter based on the number of good parties;

[0148] Determine the number of compatible Chinese medicines, and determine the individual length parameters based on the number of compatible Chinese medicines;

[0149] The initial population is determined based on the population size parameter and the individual length parameter.

[0150] In a possible implementation, the good party selection module 705 is further configured to:

[0151] Determine the good and bad recipes based on the initial population and fitness function;

[0152] Reduce the crossover probability and mutation probability of good and good parties, and increase the crossover probability and mutation probability of bad and good parties;

[0153] Based on the changed crossover probability and mutation probability, a genetic algorithm is used to determine the target solution.

[0154] In a possible implementation, the good party selection module 705 is further configured to:

[0155] Determine a target generation number, and after performing a genetic algorithm to the target generation number based on an initial population and a fitness function, determine at least two sub-evolution processes;

[0156] A genetic algorithm is performed in parallel based on at least two sub-evolution processes to determine a target solution.

[0157] In a possible implementation, the good party selection module 705 is further configured to:

[0158] The initial group includes the first individual and the second individual;

[0159] The compatibility scheme of the first individual and the second individual is the same; wherein the compatibility scheme includes at least two Chinese medicines, and the at least two Chinese medicines include the target Chinese medicine;

[0160] The concentration of the target Chinese medicine in the first individual is a first concentration;

[0161] The concentration of the target Chinese medicine in the second individual is a second concentration;

[0162] The first concentration and the second concentration are different.

[0163] The embodiments of this specification provide a method and device for selecting a good Chinese medicine prescription, wherein the Chinese medicine prescription selection device includes: determining background pathways, Chinese medicine sets and patient data; determining drug disorder pathways based on background pathways and Chinese medicine sets, and determining disease disorder pathways based on background pathways and patient data; determining a molecular herbal database based on disease disorder pathways and drug disorder pathways; determining an initial population based on the molecular herbal database; wherein the initial population includes multiple individuals, and the individuals include good prescriptions; determining a fitness function, and performing a genetic algorithm based on the initial population and the fitness function to determine a target good prescription. Based on the gene expression spectrum data of relevant cell lines or model animals before and after Chinese medicine intervention, and according to the enrichment analysis results of differential genes before and after Chinese medicine intervention, a new Chinese medicine prescription that can reverse the disease-related disorder pathway is found, thereby shortening the time required to find the optimal solution. At the same time, the present invention also incorporates a flexible dosage optimization algorithm, which can automatically complete the calculation of the optimal dosage ratio of each component in the good prescription for different concentration ranges of high, medium and low, thereby further improving the evaluation effect and practicality of the good prescription.

[0164] The above is a schematic scheme of a Chinese medicine prescription selection device of this embodiment. It should be noted that the technical scheme of the Chinese medicine prescription selection device and the technical scheme of the Chinese medicine prescription selection method described above belong to the same concept, and the details of the technical scheme of the Chinese medicine prescription selection device that are not described in detail can all be referred to the description of the technical scheme of the Chinese medicine prescription selection method described above.

[0165] Figure 8 The block diagram of a computing device 800 according to an embodiment of the present specification is shown. The components of the computing device 800 include but are not limited to a memory 810 and a processor 820. The processor 820 is connected to the memory 810 via a bus 830, and a database 850 is used to store data.

[0166] The computing device 800 also includes an access device 840 that enables the computing device 800 to communicate via one or more networks 860. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 840 may include one or more of any type of network interface (e.g., a network interface card (NIC)) that is wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0167] In one embodiment of the present specification, the above components of the computing device 800 and Figure 8 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 8 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0168] The computing device 800 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 800 may also be a mobile or stationary server.

[0169] Among them, the processor 820 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned method for selecting a good Chinese medicine prescription. The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the above-mentioned method for selecting a good Chinese medicine prescription belong to the same concept. The details not described in detail in the technical scheme of the computing device can be found in the description of the technical scheme of the above-mentioned method for selecting a good Chinese medicine prescription.

[0170] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned method for selecting a good Chinese medicine prescription.

[0171] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the above-mentioned method for selecting a good Chinese medicine prescription belong to the same concept, and the details not described in detail in the technical scheme of the storage medium can be found in the description of the technical scheme of the above-mentioned method for selecting a good Chinese medicine prescription.

[0172] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned method for selecting a good Chinese medicine prescription.

[0173] The above is a schematic scheme of a computer program of this embodiment. It should be noted that the technical scheme of the computer program and the technical scheme of the above-mentioned method for selecting a good Chinese medicine prescription are of the same concept, and the details not described in detail in the technical scheme of the computer program can be found in the description of the technical scheme of the above-mentioned method for selecting a good Chinese medicine prescription.

[0174] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0175] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0176] It should be noted that, for the convenience of description, the aforementioned method embodiments are all described as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the embodiments of this specification.

[0177] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0178] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A method for selecting a good Chinese medicine prescription, characterized in that: include: Identify background pathways, TCM collections, and patient data; determining a drug disorder pathway based on the background pathway and the Chinese medicine collection, and determining a disease disorder pathway based on the background pathway and the patient data; Determining a molecular herbal database based on the disease disorder pathway and the drug disorder pathway; Determine an initial population based on the molecular herbal database; wherein the initial population includes a plurality of individuals, and the individuals include Shan Fang; A fitness function is determined, and a genetic algorithm is performed based on the initial population and the fitness function to determine a target fitness method.

2. The method according to claim 1, characterized in that The determining of the drug disorder pathway based on the background pathway and the Chinese medicine set comprises: Intervening cells with the Chinese medicine collection to determine drug expression profile data; Gene enrichment analysis is performed based on the drug expression profile data and the background pathway to determine the drug disorder pathway.

3. The method according to claim 1, characterized in that The determining of the drug disorder pathway based on the background pathway and the Chinese medicine set comprises: Perform differential gene analysis based on patient data to determine disease expression profile data; A disease dysregulated pathway is determined based on the disease expression profile data and the background pathway.

4. The method according to claim 1, characterized in that The determining of the initial population based on the molecular herbal database comprises: determining a number of good parties, and determining a population size parameter based on the number of good parties; Determine the number of Chinese medicines to be combined, and determine the individual length parameters based on the number of Chinese medicines to be combined; An initial population is determined based on the population size parameter and the individual length parameter.

5. The method according to claim 1, characterized in that The performing of a genetic algorithm based on the initial population and the fitness function to determine a target fitness method includes: Determine a good recipe and a bad recipe based on the initial population and the fitness function; Reduce the crossover probability and mutation probability of the good recipe, and increase the crossover probability and mutation probability of the bad recipe; Based on the changed crossover probability and mutation probability, a genetic algorithm is used to determine the target solution.

6. The method according to claim 1, characterized in that The performing of a genetic algorithm based on the initial population and the fitness function to determine a target fitness method includes: Determining a target generation number, after performing the genetic algorithm based on the initial population and the fitness function to the target generation number, determining at least two sub-evolution processes; A genetic algorithm is performed in parallel based on the at least two sub-evolution processes to determine a target solution.

7. The method according to claim 1, characterized in that The initial group includes a first individual and a second individual; The compatibility scheme of the first individual and the second individual is the same; wherein the compatibility scheme includes at least two Chinese medicines, and the at least two Chinese medicines include the target Chinese medicine; The concentration of the target traditional Chinese medicine in the first individual is a first concentration; The concentration of the target Chinese medicine in the second individual is a second concentration; The first concentration and the second concentration are different.

8. A device for selecting a good Chinese medicine prescription, characterized in that: include: A data determination module configured to determine background pathways, a set of traditional Chinese medicines, and patient data; a pathway determination module configured to determine a drug disorder pathway based on the background pathway and the Chinese medicine set, and to determine a disease disorder pathway based on the background pathway and the patient data; a database determination module, configured to determine a molecular herbal database based on the disease disorder pathway and the drug disorder pathway; A population determination module is configured to determine an initial population based on the molecular herbal database; wherein the initial population includes a plurality of individuals, and the individuals include Shan Fang; The good prescription selection module is configured to determine a fitness function, and to perform a genetic algorithm based on the initial population and the fitness function to determine a target good prescription.

9. A computing device, characterized in that include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for selecting a good Chinese medicine prescription as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for selecting a good Chinese medicine prescription as described in any one of claims 1 to 7.

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