Data prediction method and device for RNA virus key variation sites

Abstract

The invention discloses a data prediction method and device for RNA virus key variation sites, and the method specifically comprises the steps: constructing a virus evolution branch tree corresponding to a target virus according to the obtained historical virus evolution data corresponding to the target virus; for each nucleotide site in the target virus gene sequence, a first mutation probability and a second mutation probability corresponding to the nucleotide site are determined based on the virus evolution branch tree. And determining key variation sites in the gene sequence of the target virus according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus. By means of the method, the discovery efficiency of the key variation sites in the virus gene sequence can be effectively improved, the time cost and resource consumption are saved, and then the subsequent research and development efficiency of vaccines or drugs aiming at the key variation sites in the gene sequence is indirectly and remarkably improved; and the body health and life safety of the masses are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to a method and device for predicting data on key mutation sites of RNA viruses. Background Art

[0002] Viruses are a relatively unique type of microorganism in nature. They cannot survive and reproduce independently and must rely on host cells to complete their life activities. At present, the parasitism of most viruses usually causes a certain degree of negative impact on the host, such as causing disorders in the host's physiological system and causing serious diseases.

[0003] In the process of spreading and adapting between different hosts, viruses can undergo various genetic mutations according to different living environments and conditions to ensure their stable survival in host cells. Therefore, analyzing the mutation sites in viral genes and designing drugs are of great help in solving the problem of diseases caused by viruses.

[0004] The current traditional technology mainly involves conducting a large number of repeated experimental tests on the virus in the laboratory to determine the possible mutation sites in the viral gene sequence. Although this method can determine the more important mutation sites in the viral gene to a certain extent, the overall determination process takes a long time and is relatively inefficient, and the R&D costs of a large number of experimental tests are extremely high. While affecting the development of drugs and vaccines against the virus, it may also cause new unknown mutations in the virus due to the excessive time consumption, which may lead to more serious disease problems.

[0005] Therefore, it is crucial to efficiently and accurately determine the key mutation gene sites in the viral gene sequence. Summary of the invention

[0006] This specification provides a data prediction method and device for key mutation sites of RNA viruses to partially solve the above-mentioned problems existing in the prior art.

[0007] This manual adopts the following technical solutions: This specification provides a data prediction method for key mutation sites of RNA viruses, including: Obtaining historical virus evolution data corresponding to the target virus, wherein the historical virus evolution data at least includes: virus gene sequence data and host information of the target virus, and virus gene sequence data and host information of each historical evolution virus obtained by the historical evolution of the target virus; Constructing a virus evolution branch tree corresponding to the target virus according to the historical virus evolution data; For each nucleotide site of the target virus, based on the virus evolution branch tree, determine a first mutation probability and a second mutation probability corresponding to the nucleotide site, wherein the first mutation probability is used to indicate the probability of a fixed mutation occurring at the nucleotide site, and the second mutation probability is used to indicate the probability of a parallel mutation occurring at the nucleotide site; The key mutation sites of the target virus are determined according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus.

[0008] Optionally, constructing a virus evolution branch tree corresponding to the target virus according to the historical virus evolution data, specifically includes: Constructing a basic evolutionary tree corresponding to the target virus according to the viral gene sequence data of the target virus in the historical virus evolution data and the viral gene sequence data of each historical evolutionary virus corresponding to the target virus; According to the host information of the target virus and the host information of each historical evolution virus corresponding to the target virus, the basic evolutionary tree is subjected to host source classification and coloring processing, and the colored basic evolutionary tree is used as the virus evolution branch tree corresponding to the target virus.

[0009] Optionally, based on the virus evolution branch tree, for each nucleotide site of the target virus, determining a first mutation probability corresponding to the nucleotide site specifically includes: According to a preset fixed mutation cycle duration, the historical evolution duration of the target virus is divided into periods to determine each fixed mutation cycle corresponding to the target virus; For each fixed mutation cycle corresponding to the target virus, determine at least one virus evolution branch corresponding to the fixed mutation cycle according to the virus evolution branch tree, and determine the fixed mutation evaluation result corresponding to the target virus within the fixed mutation cycle according to the at least one virus evolution branch corresponding to the fixed mutation cycle; According to the fixed mutation evaluation results corresponding to each fixed mutation cycle, the first mutation probability corresponding to the nucleotide site is determined.

[0010] Optionally, determining the key mutation site of the target virus according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus specifically includes: For each nucleotide site of the target virus, a first mutation probability and a second mutation probability corresponding to the nucleotide site are input into a pre-trained site prediction model, so that the site prediction model determines the comprehensive site feature data corresponding to the nucleotide site according to the first mutation probability and the second mutation probability corresponding to the nucleotide site; Based on the comprehensive site feature data corresponding to each nucleotide site of the target virus, key mutation sites are determined from each nucleotide site of the target virus.

[0011] Optionally, train a site prediction model, including: Obtaining a first mutation probability and a second mutation probability corresponding to each nucleotide site of the sample virus; For each nucleotide site of the sample virus, the first mutation probability and the second mutation probability corresponding to the nucleotide site are input into the site prediction model to be trained, so that the site prediction model to be trained determines the comprehensive site feature data corresponding to the nucleotide site according to the first mutation probability and the second mutation probability corresponding to the nucleotide site, and determines the loss value of the site prediction model to be trained for the nucleotide site according to the comprehensive site feature data corresponding to the nucleotide site and the standard comprehensive site feature data corresponding to the nucleotide site, and the size of the loss value is negatively correlated with the feature similarity between the comprehensive site feature data corresponding to the nucleotide site and the comprehensive standard site feature data corresponding to the nucleotide site; According to the loss value of each nucleotide site of the sample virus for the site prediction model to be trained, the total loss value corresponding to the site prediction model to be trained is determined, and the site prediction model to be trained is trained according to the total loss value.

[0012] Optionally, determining the key mutation site of the target virus according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus specifically includes: For each nucleotide site of the target virus, a weighted summation process is performed on the first mutation probability and the second mutation probability corresponding to the nucleotide site according to a preset fixed mutation weight and a parallel mutation weight to obtain a comprehensive probability of a variant site corresponding to the nucleotide site; According to a preset comprehensive probability threshold of mutation sites and the comprehensive probability of mutation sites corresponding to each nucleotide site of the target virus, the key mutation sites corresponding to the target virus are determined from each nucleotide site of the target virus.

[0013] Optionally, determining the key mutation site of the target virus according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus specifically includes: Based on a preset site screening strategy, according to the first mutation probability and the second mutation probability of each nucleotide site of the target virus, the key mutation site corresponding to the target virus is determined from each nucleotide site of the target virus.

[0014] This specification provides a data prediction device for key mutation sites of RNA viruses, including: An acquisition module is used to acquire historical virus evolution data corresponding to the target virus, wherein the historical virus evolution data at least includes: virus gene sequence data and host information of the target virus, and virus gene sequence data and host information of each historical evolution virus obtained by the historical evolution of the target virus; A construction module, used to construct a virus evolution branch tree corresponding to the target virus according to the historical virus evolution data; A probability determination module, for each nucleotide site of the target virus, based on the virus evolution branch tree, determining a first mutation probability and a second mutation probability corresponding to the nucleotide site, wherein the first mutation probability is used to indicate the probability of a fixed mutation occurring at the nucleotide site, and the second mutation probability is used to indicate the probability of a parallel mutation occurring at the nucleotide site; The site determination module is used to determine the key mutation site of the target virus according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus.

[0015] This specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned data prediction method for key mutation sites of RNA viruses.

[0016] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned data prediction method for key mutation sites of RNA viruses is implemented.

[0017] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects: It can be seen from the above method that in the data prediction method for key mutation sites of RNA viruses provided in this specification, the virus evolution branch tree corresponding to the target virus can be constructed based on the historical virus evolution data corresponding to the target virus obtained. Then, for each nucleotide site in the target virus gene sequence, the first mutation probability and the second mutation probability corresponding to the nucleotide site are determined based on the virus evolution branch tree. Finally, the key mutation sites in the target virus gene sequence are determined based on the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus.

[0018] From the above content, it can be seen that the data prediction method for key mutation sites of RNA viruses provided in this specification can determine the key mutation sites in the target virus gene sequence that may have a serious impact on viral gene mutations based on the historical virus evolution data corresponding to the target virus. This method can effectively save the time cost and resource consumption spent on discovering and determining key mutation sites in viral gene sequences, effectively improve the efficiency of discovering key mutation sites in viral gene sequences, and indirectly make the subsequent research and development efficiency of vaccines or drugs for key mutation sites in gene sequences significantly improved, ensuring the health and life safety of the people. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings: Figure 1 A schematic diagram of a data prediction method for key mutation sites of RNA viruses provided in this specification; Figure 2 A schematic diagram of the overall process framework of a data prediction method for key mutation sites of RNA viruses provided in this specification; Figure 3 A schematic diagram of a data prediction device for key mutation sites of RNA viruses provided in this specification; Figure 4 A method provided in this specification corresponding to Figure 1 Schematic diagram of the structure of an electronic device. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0021] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0022] Figure 1 A schematic diagram of a data prediction method for key mutation sites of RNA viruses provided in this specification, comprising the following steps: S101: Obtain historical virus evolution data corresponding to the target virus.

[0023] In the current natural world, viruses are a relatively unique microorganism. Due to their unique way of survival, they can only rely on parasitism, which inevitably has more or less impact on the physiological activities of the host. During the transmission process, viruses will undergo genetic mutations in order to adapt to different living environments and conditions, which helps them to survive stably in the host. Therefore, analyzing the mutation sites in viral genes plays a vital role in the development of drugs and vaccines to overcome diseases caused by viruses.

[0024] At present, most traditional methods rely on a large number of repeated experiments and tests in the laboratory, which is time-consuming and costly, and may cause new unknown mutations in the virus due to time delays, increasing the risk of more serious problems. Therefore, how to efficiently determine the key mutation gene sites from the viral gene sequence is an urgent problem to be solved.

[0025] To this end, this specification provides a data prediction method for key mutation sites of RNA viruses, wherein the execution subject used by the method provided in this specification can be a terminal device such as a desktop computer, a laptop computer, or a server. In addition, the execution subject of this specification can also be a subject in the form of software, such as a client installed in a terminal device, etc. For the sake of ease of explanation, this specification below only uses the terminal device as the execution subject to explain the data prediction method for key mutation sites of RNA viruses provided.

[0026] Based on this, the terminal device using the data prediction method for key mutation sites of RNA viruses provided in this specification can determine the first mutation probability used to represent the probability of fixed mutations occurring at each nucleotide site in the target virus, and the second mutation probability used to represent the probability of parallel mutations occurring at each nucleotide site in the target virus, based on the historical virus evolution data corresponding to the acquired target virus. Then, the terminal device can determine the key mutation sites from the gene sequence of the target virus based on the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus.

[0027] Among them, the specific application scenario of the terminal device using the method in this specification to predict the key mutation sites of the target virus can be determined according to actual needs. For example, in the monitoring scenario for the epidemic situation, the terminal device using the method in this specification can determine the key mutation sites in the gene sequence of the target virus that may mutate in the future propagation process based on the historical virus evolution data corresponding to the target virus. The relevant information data of the determined key mutation sites, such as protein structure information of similar nucleotide sites, host information of mutant viruses, etc., can be displayed to relevant professional medical personnel for relevant professional medical personnel to refer to data for the spread of the disease and the possible direction of future virus mutation. Through the accurate prediction of key mutation sites in the target, it can play a very beneficial role in early warning of the epidemic situation and the rational control of the spread and outbreak of the epidemic.

[0028] For another example, in the field of vaccine development for diseases caused by target viruses, the terminal device using the method of this specification can determine the key mutation sites in the gene sequence of the target virus that cause serious diseases based on the historical virus evolution data corresponding to the target virus. By recommending the information of the determined key mutation sites to the medical researchers of the relevant virus vaccines, it can provide effective assistance to the medical researchers of the virus vaccines when developing vaccines or drugs for the mutant viruses of the target virus. To a certain extent, it promotes the development rate of vaccines and drugs, which is conducive to solving the disease problems caused by virus transmission and protecting the health and life safety of the people.

[0029] In this specification, the terminal device can obtain the historical virus evolution data corresponding to the target virus. The historical virus evolution data may at least include: the virus gene sequence data of the target virus and the host information corresponding to the target virus, as well as the virus gene sequence of each historical evolution virus corresponding to the target virus in the historical evolution process and the host information corresponding to each historical evolution virus.

[0030] It should be noted that in this specification, the historical virus evolution data corresponding to the target virus can be updated in fact. As time goes by, the gene sequence of the virus may undergo various gene mutations that cannot be intuitively predicted under the influence of different living environments, changing living conditions and diverse parasitic hosts. By updating the historical virus evolution data in real time, it is ensured that the various data determined in the subsequent process and the key mutation sites finally predicted have extremely strong real-time effects and practicality.

[0031] In addition, in addition to the above-mentioned viral gene sequence data and host information of the target virus and its historical evolutionary viruses, the historical virus evolutionary data corresponding to the target virus may also include the separation determination time and separation determination location of the target virus and each historical evolutionary virus, and the protein structure corresponding to each nucleotide site in the gene sequence. Such data can play a certain beneficial role in the subsequent prediction process of the key mutation sites of the target gene, making the determined key mutation sites more accurate and the information more comprehensive.

[0032] S102: Constructing a virus evolution branch tree corresponding to the target virus according to the historical virus evolution data.

[0033] S103: For each nucleotide site of the target virus, determine a first mutation probability and a second mutation probability corresponding to the nucleotide site based on the virus evolution branch tree.

[0034] In this specification, the terminal device can construct a virus evolution branch tree corresponding to the target virus based on the historical virus evolution data corresponding to the target virus obtained through the above steps.

[0035] Specifically, the terminal device can construct a basic evolutionary tree corresponding to the target virus based on the viral gene sequence data of the target virus in the historical virus evolution data and the viral gene sequence data of each historical evolutionary virus corresponding to the target virus. Then, the terminal device can color each virus branch in the basic evolutionary tree according to the host source corresponding to the host information based on the host information of the target virus and the host information of each historical evolutionary virus. The terminal device can use the colored basic evolutionary tree as the virus evolution branch tree corresponding to the target virus.

[0036] Furthermore, for each nucleotide site in the gene sequence of the target virus, the terminal device can determine the first mutation probability and the second mutation probability corresponding to each nucleotide site based on the virus evolution branch tree constructed above. The first mutation probability can be used to specifically indicate the probability of a fixed mutation occurring at the nucleotide site, while the second mutation probability can be used to specifically indicate the probability of a parallel mutation occurring at the nucleotide site.

[0037] Regarding the specific process of determining the first mutation probability corresponding to each nucleotide site of the target virus: First, the terminal device can divide the historical evolution time corresponding to the target virus into periods according to the preset fixed mutation cycle duration, thereby determining each fixed mutation cycle corresponding to the target virus.

[0038] Next, the terminal device can determine at least one virus evolution branch corresponding to the target virus in each fixed mutation cycle of the target virus based on the virus evolution branch tree corresponding to the target virus constructed above. The terminal device can determine the fixed mutation evaluation result corresponding to the target virus in the fixed mutation cycle based on the relevant data corresponding to at least one virus evolution branch corresponding to the target virus in the fixed mutation cycle. Among them, the fixed mutation evaluation result can be specifically used to indicate which nucleotide sites in the gene sequence of the target virus may have undergone fixed mutations in the fixed mutation cycle.

[0039] Then, the terminal device can determine the first mutation probability corresponding to each nucleotide site in the target virus, that is, the probability of a fixed mutation occurring at each nucleotide site, based on the corresponding fixed mutation evaluation results of the target virus in each fixed mutation cycle.

[0040] In the current field of virus evolution, viruses can gradually adapt to changing living environments in host cells by undergoing fixed mutations according to changes in the living environment. Fixed mutations can fundamentally change the gene sequence of the virus, so that the offspring of the virus population inherit the results of fixed mutations at birth. Therefore, reasonable research on fixed mutations of viruses can play a great role in the subsequent development of vaccines and drugs for target viruses.

[0041] The first mutation probability determined through the above steps can effectively reflect the fixed mutation probability of each nucleotide site in the target virus in the history of virus evolution. The obvious manifestation of the fixed mutation trend can effectively help determine the subsequent key mutation sites, and improve the accuracy and practicality of the subsequent determination of key mutation sites. The specific determination process of the first mutation probability can refer to the following formula: Among them, for each nucleotide site in the target virus , Used to indicate the nucleotide position The corresponding first mutation frequency. Used to indicate the historical evolution time of the target virus. It is used to represent the historical evolution time according to the preset fixed mutation cycle time The fixed mutation cycles are divided. Used to indicate nucleotide positions In a fixed mutation cycle The fixed mutation times within the period, that is, the fixed mutation cycle mentioned above The nucleotide site in at least one viral evolutionary branch corresponding to the target virus Corresponding fixed mutation evaluation results, This means that in a fixed mutation cycle The total number of mutations of the target virus.

[0042] The above formula is used for each fixed mutation cycle Each nucleotide position in the target virus The corresponding fixed mutation probability is calculated by the terminal device, and the average is calculated to obtain the probability of each nucleotide site in the target virus. The corresponding first mutation frequency .

[0043] While determining the first mutation probability corresponding to each nucleotide site of the target virus, the terminal device can also determine the second mutation probability corresponding to each nucleotide site based on the virus evolution branch tree constructed above. The so-called second mutation probability can be specifically used to indicate the probability of parallel mutations occurring at the nucleotide sites in the target virus.

[0044] It should be noted that the difference between the second mutation probability and the first mutation probability determination process is that the second mutation probability specifically reflects the occurrence of parallel mutations, which is reflected by studying each evolutionary branch of the target virus, and is not similar to each mutation cycle targeted in the first mutation probability determination process. In the current field of virus evolution, parallel mutations and the fixed mutations mentioned above are both important aspects of virus evolution research. The gene sequences of viruses in different host populations have undergone the same mutations due to the same selection pressure. This type of mutation process is called parallel mutation. Parallel mutations exist almost everywhere in different populations and evolutionary branches, and not necessarily in all virus individuals, but the same mutations may occur when facing the same or similar living environment and pressure. Fixed mutations are reflected in a single population, where all virus individuals carry genetic mutations and tend to be stably inherited.

[0045] The emergence of parallel mutations can indicate that the virus is adapting to the current living environment and selection pressure, so that it can better parasitize the host cell. Therefore, studying and determining the probability of parallel mutations at each nucleotide site can effectively improve the accuracy of the key mutation site determination process. As for the specific determination process of the above-mentioned second mutation probability, the following formula can be used as a reference: Among them, for each nucleotide site in the target virus , Used to indicate nucleotide positions The corresponding second mutation probability. Used to indicate the number of evolutionary branches in the historical evolution of the target virus. The number of evolutionary branches Each evolutionary branch in . Used to indicate nucleotide positions In the evolutionary branch The number of parallel mutations that occurred in It is used to indicate the target virus in the evolutionary branch The total number of mutation events that occurred in .

[0046] By calculating the above formula, the terminal device can determine each nucleotide site in the target virus. In each evolutionary branch The corresponding parallel mutation probability is obtained by summing and averaging the parallel mutation probabilities to obtain the probability of each nucleotide site. The corresponding second mutation probability. The determined second mutation probability can effectively reflect the actual situation of parallel mutations at each nucleotide site in the target virus during the historical evolution process. Combining it with the first mutation probability determined in the above process not only takes into account the impact of fixed mutations on the evolution of the target virus, but also integrates the relevant data of parallel mutations, effectively improving the accuracy of the subsequent process of determining key mutation sites in the target virus.

[0047] S104: Determine the key mutation site of the target virus according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus.

[0048] In this specification, the terminal device can screen out key mutation sites of the target virus from each nucleotide site of the target virus based on the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus determined through the above steps.

[0049] Specifically, for each nucleotide site in the target virus gene sequence, the terminal device can input the first mutation probability and the second mutation probability corresponding to the nucleotide site into a pre-trained site prediction model, so that the site prediction model can determine the comprehensive site feature data corresponding to the nucleotide site based on the first mutation probability and the second mutation probability corresponding to the nucleotide site.

[0050] Then, the terminal device can screen out key mutation sites from each nucleotide site of the target virus based on the comprehensive site feature data corresponding to each nucleotide site in the gene sequence of the target virus. The determination process and specific data form of the comprehensive site feature data corresponding to the nucleotide site can refer to the following formula: in, Used to represent each nucleotide position in the target virus The corresponding comprehensive site feature data can be specifically expressed in the form of a vector matrix as shown in the above formula. and are used to represent the nucleotide positions determined by the above steps. The corresponding first mutation probability and second mutation probability.

[0051] Through the above formula, the terminal device can convert the nucleotide site The corresponding first mutation probability and the second mutation probability are input into the pre-trained site prediction model so that the site prediction model can be based on the nucleotide site The corresponding first mutation probability and the second mutation probability , and then perform feature processing and feature data fusion to obtain nucleotide sites Corresponding comprehensive site feature data .

[0052] It should be noted that the historical virus evolution data corresponding to the target virus mentioned in the above steps may also include the separation determination time and separation determination location of each historical evolution virus corresponding to the target virus, the target virus, and the protein structure corresponding to each nucleotide site in the gene sequence of each historical evolution virus.

[0053] In this specification, in addition to inputting the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus into the site prediction model to determine the comprehensive site feature data corresponding to each nucleotide site, the terminal device can also input other data in the above-mentioned historical virus evolution data into the site prediction model, so that the site prediction model can determine the comprehensive site feature data corresponding to each nucleotide site not only based on the first mutation probability and the second mutation probability corresponding to each nucleotide site, but also based on the separation determination time and separation determination location of each historical evolution virus corresponding to the target virus, the target virus, and the protein structure corresponding to each nucleotide site in the gene sequence of each historical evolution virus.

[0054] The information and data involved in the comprehensive site feature data determined by this method are richer, which makes the subsequent prediction process of key mutation sites in the target virus more accurate. The determination process and specific data form of the comprehensive site feature data corresponding to this method can refer to the following formula: in, , as well as This is the same as the meaning expressed in the above formula for determining the comprehensive site feature data based only on the first mutation probability and the second mutation probability. Used to represent each nucleotide position in the target virus The corresponding comprehensive site feature data can be specifically expressed as a vector matrix as shown in the above formula, and and are used to represent the nucleotide positions determined by the above steps. The corresponding first mutation probability and second mutation probability.

[0055] In the above formula is used to indicate nucleotide positions The corresponding genotype characteristic data, specifically expressed as nucleotide positions The corresponding genetic composition or protein structure. is used to indicate nucleotide positions The corresponding geographic location feature data, specifically expressed as nucleotide sites Geographical distribution of virus samples when mutations occurred and when they were successfully isolated. is used to indicate nucleotide positions Corresponding host type characteristic data, specifically expressed as nucleotide positions The host information of the historical evolutionary virus corresponding to the target virus after mutation, such as whether the host is human, animal, etc. is used to indicate nucleotide positions The structural feature data in the gene sequence of the target virus can be specifically expressed as the position of the nucleotide site in the protein structure of the target virus and the spatial domain information of its position.

[0056] Through the calculation of the above formula, the terminal device can use the site prediction model to extract the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus, as well as the genotype feature data, geographic location feature data, host type feature data, structural feature data and other related data, and then perform feature matrix fusion to obtain the comprehensive site feature data corresponding to each nucleotide site.

[0057] After determining the comprehensive site characteristic data corresponding to each nucleotide site in the genetic sequence of the target virus, the terminal device can screen out the key mutation sites corresponding to the target virus from each nucleotide site of the target virus based on the comprehensive site characteristic data corresponding to each nucleotide site and the preset key mutation site characteristic evaluation criteria.

[0058] It should be noted that the specific model category of the site prediction model mentioned in the above method is not strictly limited in this specification, and can be a mathematical model with the ability to extract features based on data, such as a deep neural network (DNN). According to the actual application scenario and requirements, the model category of the site prediction model can be flexibly selected and set.

[0059] In addition, the site prediction model mentioned in the above process is a pre-trained mathematical model. Regarding the specific training process of the site prediction model, specifically, the terminal device can obtain the first mutation probability and the second mutation probability corresponding to each nucleotide site of the sample virus. Then, the terminal device can input the first mutation probability and the second mutation probability corresponding to each nucleotide site of the sample virus into the site prediction model to be trained, so that the site prediction model to be trained can determine the comprehensive site feature data corresponding to the nucleotide site based on the first mutation probability and the second mutation probability corresponding to the nucleotide site.

[0060] Next, the terminal device can determine the loss value of the site prediction model to be trained for the nucleotide site based on the comprehensive site feature data corresponding to the nucleotide site and the standard comprehensive site feature data corresponding to the nucleotide site. Among them, the size of the loss value is negatively correlated with the similarity between the comprehensive site feature data corresponding to the nucleotide site and the standard comprehensive site feature data. Then the terminal device can determine the total loss value of the site prediction model to be trained for the sample virus based on the loss values ​​corresponding to each nucleotide site of the sample virus. Finally, the terminal device can perform model training on the site prediction model to be trained based on the total loss value. The trained site prediction model is used to implement the above-mentioned data prediction method for key mutation sites of RNA viruses.

[0061] It should also be noted that the present specification does not strictly limit the specific training and optimization process for the site prediction model to be trained. For example, in the present specification, the optimization training process for the site prediction model can choose to use the Gradient Boosting Decision Tree (GBDT) machine learning algorithm, which can be specifically referred to the following formula: Among them, for each nucleotide site in the sample virus, Used to indicate nucleotide positions The corresponding comprehensive site feature data, It is used to indicate the first A decision tree, is the learning rate set for the site prediction model to be trained. By calculating and summing this formula, the terminal device can determine the location of each nucleotide site in the sample virus. The corresponding loss value , by optimizing all nucleotide positions The corresponding loss value , so that the ability of the site prediction model is improved, thereby obtaining a trained site prediction model.

[0062] In addition to the above training methods, in this specification, the Random Forest model training method can also be used to train and optimize the site prediction model to be trained. The specific training process can refer to the following formula: As shown in the above formula, the process of determining the overall loss value is similar to the method of gradient boosting decision tree, but the preset learning rate is removed. Instead of setting, we choose to perform average processing and average the nucleotide sites in each decision tree corresponding to the site prediction model. The average loss value of , as the nucleotide position The loss value during the entire training process.

[0063] In this specification, in addition to the above method of determining the key mutation sites in the target virus through the site prediction model, the terminal device can also directly perform a weighted summation of the first mutation probability and the second mutation probability of each nucleotide site in the target virus to determine the comprehensive probability of the mutation site corresponding to each nucleotide site. Then, the terminal device can determine the key mutation site corresponding to the target virus from each nucleotide site of the target virus according to the comprehensive probability of the mutation site corresponding to each nucleotide site and the preset comprehensive probability threshold of the mutation site.

[0064] The specific determination process of the comprehensive probability of the mutation site corresponding to each nucleotide site in the target virus mentioned above can refer to the following formula: Among them, for each nucleotide site in the target virus, and The corresponding nucleotide positions are The corresponding first mutation probability and second mutation probability. and For nucleotide sites The corresponding first mutation probability and the second mutation probability The probability weight parameters set up sum to 1, that is .

[0065] The terminal device can calculate the first mutation probability of each nucleotide site by the above calculation formula: and the second mutation probability The weighted summation is performed to obtain the comprehensive probability of the mutation site corresponding to each nucleotide site. Then, the terminal device can use the nucleotide site with a mutation site comprehensive probability greater than the mutation site comprehensive probability threshold as the key mutation site corresponding to the target virus according to the preset mutation site comprehensive probability threshold.

[0066] In addition to determining the key mutation sites corresponding to the target virus through the site prediction model and weighted summation method, the terminal device can also directly perform site screening based on the first mutation probability and the second mutation probability corresponding to each nucleotide site and the preset key mutation site evaluation strategy to determine the key mutation sites in the target virus gene sequence.

[0067] The specific key variant site evaluation strategy is not strictly limited in this specification, and may be, for example, a corresponding probability threshold is set for the first mutation probability and the second mutation probability corresponding to each nucleotide site, and when a certain mutation probability is greater than the probability threshold, the nucleotide site is considered to be a key variant site, etc. The specific content of the key variant site evaluation strategy can be set according to the actual application scenario and requirements.

[0068] It should be noted that the above three implementation methods for determining the key variant sites of the target virus according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus: through the site prediction model, weighted summation of the mutation probability, and screening according to the key variant site evaluation strategy. In practical applications, one or more methods can be arbitrarily selected according to the actual application scenario and needs. The specific combination and selection of methods are not strictly limited in this specification. The key variant sites corresponding to the target virus obtained by a variety of methods can be used for the evaluation and inspection standards of the prediction accuracy between the various methods. Thereby, the accuracy and practicality of the key variant sites finally determined are higher.

[0069] At this point, the data prediction process of the terminal device for the key mutation sites of RNA viruses has been completed. In order to facilitate the understanding of the process of the overall prediction process, the following is an overall process framework diagram of the data prediction method for the key mutation sites of RNA viruses for explanation. Figure 2 as shown in .

[0070] Figure 2 The present invention is a schematic diagram of the overall process framework of a data prediction method for key mutation sites of RNA viruses provided in this specification.

[0071] As Figure 2As shown, the terminal device can construct a virus evolution branch tree corresponding to the target virus based on the acquired historical virus evolution data corresponding to the target virus. Then, the terminal device can determine the first mutation probability and the second mutation probability corresponding to each nucleotide site in the target virus gene sequence based on the virus evolution branch tree. Finally, the terminal device can screen out the corresponding key mutation sites from the gene sequence of the target virus based on the first mutation probability and the second mutation probability corresponding to each nucleotide site in the target virus.

[0072] From the above content, it can be seen that the data prediction method for key mutation sites of RNA viruses provided in this specification can determine the key mutation sites in the target virus gene sequence that may have a serious impact on viral gene mutations based on the historical virus evolution data corresponding to the target virus. This method can effectively improve the efficiency of discovering key mutation sites in viral gene sequences, greatly saving time costs and resource consumption, and thus significantly improving the efficiency of subsequent research and development of vaccines or drugs for key mutation sites in gene sequences.

[0073] The above are one or more implementation methods of this specification. Based on the same idea, this specification also provides a corresponding data prediction device for key mutation sites of RNA viruses, such as Figure 3 shown.

[0074] Figure 3 A schematic diagram of a data prediction device for key mutation sites of RNA viruses provided in this specification, comprising: The acquisition module 301 is used to acquire historical virus evolution data corresponding to the target virus, wherein the historical virus evolution data at least includes: virus gene sequence data and host information of the target virus, and virus gene sequence data and host information of each historical evolution virus obtained by the historical evolution of the target virus; A construction module 302 is used to construct a virus evolution branch tree corresponding to the target virus according to the historical virus evolution data; A probability determination module 303 is used to determine, for each nucleotide site of the target virus, a first mutation probability and a second mutation probability corresponding to the nucleotide site based on the virus evolution branch tree, wherein the first mutation probability is used to indicate the probability of a fixed mutation occurring at the nucleotide site, and the second mutation probability is used to indicate the probability of a parallel mutation occurring at the nucleotide site; The site determination module 304 is used to determine the key mutation site of the target virus according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus.

[0075] Optionally, the construction module 302 is specifically used to construct a basic evolutionary tree corresponding to the target virus based on the viral gene sequence data of the target virus in the historical virus evolution data and the viral gene sequence data of each historical evolutionary virus corresponding to the target virus; perform host source classification and coloring on the basic evolutionary tree based on the host information of the target virus and the host information of each historical evolutionary virus corresponding to the target virus, and use the colored basic evolutionary tree as the virus evolution branch tree corresponding to the target virus.

[0076] Optionally, the probability determination module 303 is specifically used to divide the historical evolution time of the target virus into periods according to a preset fixed mutation cycle duration, and determine the fixed mutation cycles corresponding to the target virus; for each fixed mutation cycle corresponding to the target virus, determine at least one virus evolution branch corresponding to the fixed mutation cycle according to the virus evolution branch tree, and determine the fixed mutation evaluation result corresponding to the target virus within the fixed mutation cycle according to the at least one virus evolution branch corresponding to the fixed mutation cycle; determine the first mutation probability corresponding to the nucleotide site according to the fixed mutation evaluation results corresponding to each fixed mutation cycle.

[0077] Optionally, the site determination module 304 is specifically used to, for each nucleotide site of the target virus, input the first mutation probability and the second mutation probability corresponding to the nucleotide site into a pre-trained site prediction model, so that the site prediction model determines the comprehensive site characteristic data corresponding to the nucleotide site based on the first mutation probability and the second mutation probability corresponding to the nucleotide site; and determines the key mutation sites from each nucleotide site of the target virus based on the comprehensive site characteristic data corresponding to each nucleotide site of the target virus.

[0078] Optionally, the site determination module 304 is specifically used to obtain the first mutation probability and the second mutation probability corresponding to each nucleotide site of the sample virus; for each nucleotide site of the sample virus, the first mutation probability and the second mutation probability corresponding to the nucleotide site are input into the site prediction model to be trained, so that the site prediction model to be trained determines the comprehensive site feature data corresponding to the nucleotide site according to the first mutation probability and the second mutation probability corresponding to the nucleotide site, and determines the loss value of the site prediction model to be trained for the nucleotide site according to the comprehensive site feature data corresponding to the nucleotide site and the standard comprehensive site feature data corresponding to the nucleotide site, and the size of the loss value is negatively correlated with the feature similarity between the comprehensive site feature data corresponding to the nucleotide site and the comprehensive standard site feature data corresponding to the nucleotide site; according to the loss value of the site prediction model to be trained for each nucleotide site of the sample virus, the total loss value corresponding to the site prediction model to be trained is determined, and the site prediction model to be trained is model trained according to the total loss value.

[0079] Optionally, the site determination module 304 is specifically used to, for each nucleotide site of the target virus, perform weighted sum processing on the first mutation probability and the second mutation probability corresponding to the nucleotide site according to preset fixed mutation weights and parallel mutation weights, to obtain a comprehensive probability of the variant site corresponding to the nucleotide site; and determine the key variant site corresponding to the target virus from each nucleotide site of the target virus according to a preset comprehensive probability threshold of the variant site and the comprehensive probability of the variant site corresponding to each nucleotide site of the target virus.

[0080] Optionally, the site determination module 304 is specifically used to determine the key mutation site corresponding to the target virus from each nucleotide site of the target virus based on a preset site screening strategy and according to the first mutation probability and the second mutation probability of each nucleotide site of the target virus.

[0081] This specification also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 Provides a data prediction method for key mutation sites of RNA viruses.

[0082] This manual also provides Figure 4 The one shown corresponds to Figure 1 A schematic diagram of the electronic device. Figure 4As shown, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The data prediction method for key mutation sites of RNA viruses.

[0083] In the 1990s, it was very clear whether the improvement of a technology was hardware improvement (for example, improvement of the circuit structure of diodes, transistors, switches, etc.) or software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements of the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be implemented with hardware entity modules. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask chip manufacturers to design and make dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0084] The controller may be implemented in any suitable manner, for example, the controller may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (e.g., software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, and the memory controller may also be implemented as part of the control logic of the memory. It is also known to those skilled in the art that, in addition to implementing the controller in a purely computer-readable program code manner, the controller may be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller may be considered as a hardware component, and the devices for implementing various functions included therein may also be considered as structures within the hardware component. Or even, the devices for implementing various functions may be considered as both software modules for implementing the method and structures within the hardware component.

[0085] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0086] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0087] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0091] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0092] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0093] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0094] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0095] It should be understood by those skilled in the art that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0097] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0098] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A data prediction method for key mutation sites of RNA viruses, characterized in that: include: Acquire historical virus evolution data corresponding to the target virus, wherein the historical virus evolution data at least includes: virus gene sequence data and host information of the target virus, and virus gene sequence data and host information of each historical evolution virus obtained by the historical evolution of the target virus; Constructing a virus evolution branch tree corresponding to the target virus according to the historical virus evolution data; For each nucleotide site of the target virus, based on the virus evolution branch tree, determine a first mutation probability and a second mutation probability corresponding to the nucleotide site, wherein the first mutation probability is used to indicate the probability of a fixed mutation occurring at the nucleotide site, and the second mutation probability is used to indicate the probability of a parallel mutation occurring at the nucleotide site; The key mutation sites of the target virus are determined according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus.

2. The method according to claim 1, characterized in that According to the historical virus evolution data, a virus evolution branch tree corresponding to the target virus is constructed, specifically including: Constructing a basic evolutionary tree corresponding to the target virus according to the viral gene sequence data of the target virus in the historical virus evolution data and the viral gene sequence data of each historical evolutionary virus corresponding to the target virus; According to the host information of the target virus and the host information of each historical evolution virus corresponding to the target virus, the basic evolutionary tree is subjected to host source classification and coloring processing, and the colored basic evolutionary tree is used as the virus evolution branch tree corresponding to the target virus.

3. The method according to claim 1, characterized in that Based on the virus evolution branch tree, for each nucleotide site of the target virus, determining a first mutation probability corresponding to the nucleotide site specifically includes: According to a preset fixed mutation cycle duration, the historical evolution duration of the target virus is divided into periods to determine each fixed mutation cycle corresponding to the target virus; For each fixed mutation cycle corresponding to the target virus, determine at least one virus evolution branch corresponding to the fixed mutation cycle according to the virus evolution branch tree, and determine the fixed mutation evaluation result corresponding to the target virus within the fixed mutation cycle according to the at least one virus evolution branch corresponding to the fixed mutation cycle; According to the fixed mutation evaluation results corresponding to each fixed mutation cycle, the first mutation probability corresponding to the nucleotide site is determined.

4. The method according to claim 1, characterized in that Determining the key mutation sites of the target virus according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus specifically includes: For each nucleotide site of the target virus, a first mutation probability and a second mutation probability corresponding to the nucleotide site are input into a pre-trained site prediction model, so that the site prediction model determines the comprehensive site feature data corresponding to the nucleotide site according to the first mutation probability and the second mutation probability corresponding to the nucleotide site; Based on the comprehensive site feature data corresponding to each nucleotide site of the target virus, key mutation sites are determined from each nucleotide site of the target virus.

5. The method according to claim 4, characterized in that Training site prediction models, including: Obtaining a first mutation probability and a second mutation probability corresponding to each nucleotide site of the sample virus; For each nucleotide site of the sample virus, the first mutation probability and the second mutation probability corresponding to the nucleotide site are input into the site prediction model to be trained, so that the site prediction model to be trained determines the comprehensive site feature data corresponding to the nucleotide site according to the first mutation probability and the second mutation probability corresponding to the nucleotide site, and determines the loss value of the site prediction model to be trained for the nucleotide site according to the comprehensive site feature data corresponding to the nucleotide site and the standard comprehensive site feature data corresponding to the nucleotide site, and the size of the loss value is negatively correlated with the feature similarity between the comprehensive site feature data corresponding to the nucleotide site and the comprehensive standard site feature data corresponding to the nucleotide site; According to the loss value of each nucleotide site of the sample virus for the site prediction model to be trained, the total loss value corresponding to the site prediction model to be trained is determined, and the site prediction model to be trained is trained according to the total loss value.

6. The method according to claim 1, characterized in that Determining the key mutation sites of the target virus according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus specifically includes: For each nucleotide site of the target virus, a weighted summation process is performed on the first mutation probability and the second mutation probability corresponding to the nucleotide site according to a preset fixed mutation weight and a parallel mutation weight to obtain a comprehensive probability of a variant site corresponding to the nucleotide site; According to a preset comprehensive probability threshold of mutation sites and the comprehensive probability of mutation sites corresponding to each nucleotide site of the target virus, the key mutation sites corresponding to the target virus are determined from each nucleotide site of the target virus.

7. The method according to claim 1, characterized in that Determining the key mutation sites of the target virus according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus specifically includes: Based on a preset site screening strategy, according to the first mutation probability and the second mutation probability of each nucleotide site of the target virus, the key mutation site corresponding to the target virus is determined from each nucleotide site of the target virus.

8. A data prediction device for key mutation sites of RNA viruses, characterized in that: include: An acquisition module is used to acquire historical virus evolution data corresponding to the target virus, wherein the historical virus evolution data at least includes: virus gene sequence data and host information of the target virus, and virus gene sequence data and host information of each historical evolution virus obtained by the historical evolution of the target virus; A construction module, used to construct a virus evolution branch tree corresponding to the target virus according to the historical virus evolution data; A probability determination module, for each nucleotide site of the target virus, based on the virus evolution branch tree, determining a first mutation probability and a second mutation probability corresponding to the nucleotide site, wherein the first mutation probability is used to indicate the probability of a fixed mutation occurring at the nucleotide site, and the second mutation probability is used to indicate the probability of a parallel mutation occurring at the nucleotide site; The site determination module is used to determine the key mutation site of the target virus according to the first mutation probability and the second mutation probability corresponding to each nucleotide site of the target virus.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method described in any one of claims 1 to 7 is implemented.

Images