Method, system, electronic device and medium for mining functional elements of synthetic biology

Abstract

The present invention provides a method, system, electronic device, and medium for mining synthetic biological functional elements, belonging to the field of biological element mining technology. The method constructs a database by collecting multi-source heterogeneous data and extracts feature vectors using the GCNv2 algorithm. A support vector machine is optimized using an improved particle swarm optimization algorithm to construct a biological functional element classification model. Based on user needs, the model searches the database for matching candidate biological functional elements and recommends the optimal solution using a gradient boosting tree ranking algorithm. The present invention utilizes the above-mentioned method, system, electronic device, and medium for mining synthetic biological functional elements to improve the efficiency and accuracy of synthetic biological functional element mining.

Description

Technical Field

[0001] The present invention relates to the field of biological element mining technology, and in particular to a method, system, electronic equipment and medium for mining synthetic biological functional elements. Background Art

[0002] In the field of synthetic biology, the mining of functional elements is fundamental to the study and design of biological systems. However, traditional methods for mining functional elements often face numerous challenges. On the one hand, biological data is multi-source and heterogeneous, including various types of data such as gene sequences, protein structures, and biological function descriptions. These data vary in format, scale, and complexity, making data integration and processing difficult. On the other hand, the functions of biological elements are often closely related to their complex structures and interactions. How to effectively extract and utilize these features for functional classification and mining is an urgent problem that needs to be solved.

[0003] Existing methods for discovering functional elements mostly rely on experimental verification and manual analysis, which is not only time-consuming and labor-intensive, but also fails to fully cover all possible biological elements. With the rapid development of bioinformatics, a vast amount of biological data is being generated and accumulated, providing new opportunities for discovering functional elements. However, efficiently processing this multi-source, heterogeneous data and discovering biological elements with specific functions from it remains a major challenge in the field of synthetic biology.

[0004] Furthermore, traditional classification algorithms have limitations in classifying biological component functions. Due to the complexity and diversity of biological data, traditional classification algorithms often struggle to achieve optimal results. Therefore, new algorithms and technologies are needed to improve the accuracy and efficiency of biological component functional classification.

[0005] To address the above issues, the present invention proposes a method for mining functional elements in synthetic biology, which aims to achieve efficient and accurate functional element mining and recommendation by collecting multi-source heterogeneous data, building a comprehensive database, and adopting advanced feature extraction technology and optimized classification algorithms. Summary of the Invention

[0006] The purpose of the present invention is to provide a method, system, electronic equipment and medium for mining synthetic biology functional elements, thereby improving the efficiency and accuracy of mining synthetic biology functional elements.

[0007] To achieve the above objectives, the present invention provides a method for mining functional elements of synthetic biology, comprising the following steps:

[0008] S1. Collect multi-source heterogeneous data, including gene sequences, protein structures, and biological function descriptions, pre-process the multi-source heterogeneous data, and build a database;

[0009] S2. Perform feature extraction on the processed data based on the GCNv2 feature extraction algorithm to generate biological function element feature vectors, and divide the biological function element feature vectors into a training set and a test set;

[0010] S3, introducing an improved particle swarm optimization algorithm to optimize the support vector machine, and constructing and training a biological functional element classification model based on the training set and test set in step S2;

[0011] S4. Based on the user's specific needs, the trained biological functional element classification model is used to search and match in the database, to mine candidate biological functional elements that meet the requirements, and the optimal solution is recommended based on the sorting algorithm.

[0012] Preferably, in step S1, the protein structure includes primary structure, secondary structure, tertiary structure and quaternary structure; the biological function description includes molecular function, biological process and cellular component.

[0013] Preferably, in step S2, feature extraction is performed on the processed data based on the GCNv2 feature extraction algorithm to generate a biological function element feature vector. The specific operation is as follows:

[0014] S21. Input the preprocessed data into the GCNv2 network and use the convolutional layer to extract features from the preprocessed data.

[0015] f (l+1) =σ(W (l) *f (l) +b (l) )(1);

[0016] Among them, f (l) Represents the input feature map of the lth layer; W (l) represents the weight matrix of the lth layer; b (l) represents the bias vector of the lth layer; σ represents the ReLU activation function; * represents the convolution operation; f (l+1) Represents the input feature map of the l+1th layer;

[0017] S22, using the output of step S21 (l+1) Generate probability maps and processed feature maps;

[0018] Use the softmax function to transform f (l+1) Convert to a probability plot:

[0019] o=softmax(f (l+1) )(2);

[0020] Among them, o represents the probability map;

[0021] f(l+1) Perform pooling and normalization operations to obtain the processed feature map f;

[0022] S23, upsampling the probability map to match the resolution of the original image;

[0023] output=PixeShuffle(o,r)(3);

[0024] Where r represents the upsampling factor; output represents the probability map after upsampling; PixeShuffle represents the upsampling function;

[0025] S24, performing non-maximum suppression on the upsampled probability map to determine the location of the feature points;

[0026] S25, using the determined feature point positions, performing bilinear sampling on the processed feature map f obtained in step S22 to extract feature vectors corresponding to the feature point positions;

[0027] desc=BilinearSampling(f,KL)(4);

[0028] Where desc represents the feature vector after bilinear sampling; KL represents the position of the feature point;

[0029] S26, converting the feature vector extracted in step S25 into a binary descriptor through a binarization network layer;

[0030] desc'=κ(desc)(5);

[0031] Where desc' represents the binary descriptor feature vector; κ represents the binarization function.

[0032] Preferably, in step S3, an improved particle swarm optimization algorithm is introduced to optimize the support vector machine, and the specific operations are as follows:

[0033] S31. Initialize the position X of each particle in the particle swarm i and speed V i , where i represents the index of the particle and the position represents the hyperparameters C and γ of the SVM;

[0034] Position initialization:

[0035] X i =[C i ,γ i ](6);

[0036] Speed initialization:

[0037] V i =[vC i ,vγi ](7);

[0038] Among them, C i represents the hyperparameter C corresponding to the i-th particle; γ i represents the hyperparameter γ corresponding to the i-th particle; vC i represents the velocity component of the i-th particle corresponding to the hyperparameter C; vγ i represents the velocity component of the i-th particle corresponding to the hyperparameter γ;

[0039] S32, define the fitness function, f(X i )=Accuracy(SVM(X i )), where Accuracy is the use of X i The classification accuracy of the trained SVM model;

[0040] S33, dynamically adjust the inertia weight to balance global search and local search capabilities;

[0041]

[0042] Among them, ω represents the inertia weight; ω max Indicates the maximum value of inertia weight; ω min Indicates the minimum value of the inertia weight; t indicates the current number of iterations; iter m Indicates the maximum number of iterations;

[0043] Step S34: dynamically adjust the learning factors c1 and c2 according to the relationship between the particle's historical best position and the global best position;

[0044]

[0045] Where c represents the learning factor c1 or c2; c max Indicates the maximum value of the learning factor c1 or c2; c min Indicates the minimum value of the learning factor c1 or c2;

[0046] Step S35: updating the speed and position of the particle according to the individual optimal position and the global optimal position;

[0047] V i (T+1)=ωV i (T)+c1×rand()×(pbest i -X i (T)+c2×rand()×(gbest-X i (T)))(10);

[0048] X i (T+1)=X i(T)+V i (T+1)(11);

[0049] Among them, V i (T+1) represents the velocity vector of the i-th particle at time T+1; pbest i represents the best historical position of the i-th particle; gbest represents the best historical position of the particle group; X i (T+1) represents the position vector at time T+1; V i (T+1) represents the velocity vector of the i-th particle at time T+1;

[0050] Step S36: Update the individual best position and the global best position;

[0051]

[0052] Where f(X) represents the fitness function; neighborhood (Xi) represents the position set of other particles in the neighborhood of the i-th particle;

[0053] Step S37: repeat steps S33-S36 until the maximum number of iterations is reached;

[0054] Step S38: Output the optimal hyperparameter C * and γ * , and train the final SVM model.

[0055] Preferably, in step S4, based on the specific needs of the user, the trained biological function element classification model is used to search and match in the database, to mine candidate biological function elements that meet the requirements, and to recommend the optimal solution based on the ranking algorithm. The specific operations are as follows:

[0056] S41, using the biological function element classification model trained in step S3, combined with the biological function element feature vector extracted in step S2, to search and match in the constructed database;

[0057] S42, searching for biological components that meet user requirements using association rules;

[0058] S43, after mining a plurality of candidate biological function elements, ranking the candidate biological function elements using a ranking algorithm based on a gradient boosting tree to obtain an optimal biological function element;

[0059] S44. Conduct experimental verification of the optimal biological functional element to confirm its actual function and performance;

[0060] S45, feeding back the verification results into the biological functional element classification model to update and optimize the model;

[0061] S46. Output the optimized model results to the user.

[0062] Preferably, in step S42, the association rules are used to search for biological components that meet the user's requirements. The specific operations are as follows:

[0063] S421, processing the biological function element feature vector and biological function description extracted in step S2 to obtain a data format suitable for an association rule mining algorithm;

[0064] S422, generating frequent itemsets;

[0065] Frequent itemsets = {itemsets|support(itemsets) ≥ minimum support} (13);

[0066] Among them, the item set represents the combination of the biological function element feature vector and the biological function description extracted from the database; the support (item set) represents the proportion of the item set in all records; the minimum support is a user-defined threshold used to determine whether the item set is frequent;

[0067] S423: Generate association rules;

[0068]

[0069] The antecedent represents the premise of the rule, i.e., a combination of one or more biological function element characteristics; the posterior represents the result of the rule, i.e., the biological function element characteristics associated with the antecedent.

[0070] S424: Calculate the confidence and lift, and obtain the filtered rules;

[0071]

[0072] Among them, confidence represents the probability of the occurrence of the latter under the condition that the former occurs; support (former ∪ latter) represents the support for the simultaneous occurrence of the former and the latter; support (former) represents the support for the occurrence of the former alone.

[0073]

[0074] Among them, lift represents the improvement of the support of the former for the latter relative to random selection; support (latter) represents the support of the latter when it appears alone;

[0075] S425, assigning an initial score to each candidate biological function element, the score being based on the confidence and lift of its association rule;

[0076] Rating = confidence × lift (17);

[0077] S426. Apply the filtered rules to searches based on customer-specific needs to mine candidate biological functional elements.

[0078] Preferably, in step S43, after mining a plurality of candidate biological function elements, a ranking algorithm based on a gradient boosting tree is used to sort these candidate biological function elements to obtain the optimal biological function element. The specific operation is as follows:

[0079] S431, using the biological function element feature vector extracted in step S2 as an input feature and the score of the candidate biological function element obtained in step S425 as a target variable to train a gradient boosting tree model;

[0080] S432, using the test set of step S2 to verify the gradient boosting tree model and evaluate the prediction performance of the model;

[0081] S433, applying the trained gradient boosting tree model to the candidate biological function elements screened in step S42, and ranking all candidate biological function elements according to the scores predicted by the model;

[0082] S434, selecting the element with the highest score from the ranked candidate biological function element list as the optimal biological function element;

[0083] S435 : Output the sorted candidate biological function element list and the optimal biological function element to the user.

[0084] The present invention also provides a synthetic biology functional element mining system, comprising:

[0085] The database construction module is used to collect multi-source heterogeneous data, including gene sequences, protein structures, and biological function descriptions, pre-process the multi-source heterogeneous data, and build a database;

[0086] A feature extraction module is used to extract features from the processed data based on the GCNv2 feature extraction algorithm, generate biological function element feature vectors, and divide the biological function element feature vectors into a training set and a test set;

[0087] Model training and optimization module, which is used to introduce an improved particle swarm optimization algorithm to optimize the support vector machine and build and train a biological functional element classification model based on the training set and test set;

[0088] The functional element mining and recommendation module is used to search and match in the database based on the user's specific needs using the trained biological functional element classification model, to mine candidate biological functional elements that meet the requirements, and to recommend the optimal solution based on the sorting algorithm.

[0089] The present invention also provides a computer device comprising: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned synthetic biology functional element mining method when executing the computer program.

[0090] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned synthetic biology functional element mining method when the computer program is executed by a processor.

[0091] Therefore, the present invention adopts the above-mentioned method, system, electronic device and medium for mining functional elements of synthetic biology, and the beneficial technical effects are as follows:

[0092] (1) Efficient data processing capabilities:

[0093] The present invention builds a comprehensive database by collecting and preprocessing multi-source heterogeneous data, including gene sequences, protein structures (covering primary, secondary, tertiary, and quaternary structures), and biological function descriptions (including molecular functions, biological processes, and cellular components). This data integration and preprocessing approach significantly improves the efficiency and accuracy of data processing.

[0094] (2) Advanced feature extraction technology:

[0095] The GCNv2 feature extraction algorithm is used to extract features from the processed data and generate feature vectors of biological functional components. Through convolutional layers, probability map generation, upsampling, and non-maximum suppression, the GCNv2 algorithm effectively extracts key features of biological components, providing strong support for subsequent classification and mining.

[0096] (3) Optimized model training and classification performance:

[0097] An improved particle swarm optimization algorithm was introduced to optimize support vector machines (SVMs) to construct and train a classification model for biological functional elements. This optimization algorithm dynamically adjusts the inertia weight and learning factor, balancing global and local search capabilities, thereby improving the classification accuracy and generalization ability of the SVM model.

[0098] (4) Accurate mining and recommendation functions:

[0099] Based on the user's specific needs, the trained biological functional element classification model is used to search and match within the database, identifying candidate biological functional elements that meet the requirements. Association rule search and a gradient boosting tree-based ranking algorithm are then used to further screen the optimal biological functional elements and conduct experimental verification. This precise mining and recommendation capability significantly improves the efficiency and accuracy of discovering synthetic biology functional elements. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 This is a flow chart of the method for mining functional elements of synthetic biology of the present invention;

[0101] Figure 2 This is the GCNv2 feature extraction flowchart;

[0102] Figure 3 Optimize graph for particle swarm algorithm;

[0103] Figure 4 Flowchart for association rule mining. DETAILED DESCRIPTION

[0104] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0105] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0106] Example 1

[0107] like Figure 1 FIG. 1 is a flow chart of a method for mining functional elements of synthetic biology according to the present invention, comprising the following steps:

[0108] S1. Collect multi-source heterogeneous data, including but not limited to gene sequences, protein structures (primary, secondary, tertiary and quaternary structures) and biological function descriptions (molecular functions, biological processes and cellular components) from public databases.

[0109] Preprocessing of collected data, including data cleaning (removing invalid or erroneous data entries), format conversion (unifying the data format for easier processing), and standardization (ensuring that data can be compared and analyzed under the same standards)

[0110] S2, based on the GCNv2 feature extraction algorithm, the processed data is subjected to feature extraction to generate biological function element feature vectors, and the biological function element feature vectors are divided into a training set and a test set. The specific operations are as follows ( Figure 2 shown):

[0111] S21. Input the preprocessed data into the GCNv2 network and use the convolutional layer to extract features from the preprocessed data.

[0112] f (l+1) =σ(W (l) *f (l) +b (l) )(1);

[0113] Among them, f (l) Represents the input feature map of the lth layer; W (l)represents the weight matrix of the lth layer; b (l) represents the bias vector of the lth layer; σ represents the ReLU activation function; * represents the convolution operation; f (l+1) Represents the input feature map of the l+1th layer;

[0114] S22, using the output of step S21 (l+1) Generate probability maps and processed feature maps;

[0115] Use the softmax function to transform f (l+1) Convert to a probability plot:

[0116] o=softmax(f (l+1) )(2);

[0117] Among them, o represents the probability map;

[0118] f (l+1) Perform pooling and normalization operations to obtain the processed feature map f;

[0119] S23, upsampling the probability map, and the upsampling factor is set to 2 to match the resolution of the original image;

[0120] output=PixeShuffle(o,r)(3);

[0121] Where r represents the upsampling factor; output represents the probability map after upsampling; PixeShuffle represents the upsampling function;

[0122] S24, performing non-maximum suppression on the upsampled probability map to determine the location of the feature points;

[0123] S25, using the determined feature point positions, performing bilinear sampling on the processed feature map f obtained in step S22 to extract feature vectors corresponding to the feature point positions;

[0124] desc=BilinearSampling(f,KL)(4);

[0125] Among them, desc represents the feature vector after bilinear sampling, and KL represents the position of the feature point;

[0126] S26, converting the feature vector extracted in step S25 into a binary descriptor through a binarization network layer;

[0127] desc'=κ(desc)(5);

[0128] Where desc' represents the binary descriptor feature vector; κ represents the binarization function.

[0129] S3, introduce the improved particle swarm optimization algorithm to optimize the support vector machine (such as Figure 3 As shown), based on the training set and test set of step S2, a biological functional element classification model is constructed and trained. The specific operations are as follows:

[0130] S31. Initialize the position X of each particle in the particle swarm i and speed V i , the number of particles is set to 30, where i represents the index of the particle and the position represents the hyperparameters C and γ of SVM;

[0131] Position initialization:

[0132] X i =[C i ,γ i ](6);

[0133] Speed initialization:

[0134] V i =[vC i ,vγ i ](7);

[0135] Among them, C i is the hyperparameter C corresponding to the i-th particle; γ i is the hyperparameter γ corresponding to the i-th particle; vC i represents the velocity component of the i-th particle corresponding to the hyperparameter C; vγ i represents the velocity component of the i-th particle corresponding to the hyperparameter γ;

[0136] S32, define the fitness function, f(X i )=Accuracy(SVM(X i )), where Accuracy is the use of X i The classification accuracy of the trained SVM model;

[0137] S33, dynamically adjust the inertia weight, with the initial value set to 0.9, to balance the global search and local search capabilities;

[0138]

[0139] Among them, ω represents the inertia weight; ω max Indicates the maximum value of the inertia weight, set to 0.95; ω min Indicates the minimum value of the inertia weight, which is set to 0.85; t indicates the current number of iterations; iter m Indicates the maximum number of iterations;

[0140] Step S34: dynamically adjust the learning factors c1 and c2 according to the relationship between the particle's historical best position and the global best position;

[0141]

[0142] Where c represents the learning factor c1 or c2; c max Indicates the maximum value of the learning factor c1 or c2, set to 2.5; c min Indicates the minimum value of the learning factor c1 or c2, set to 1.5;

[0143] Step S35: updating the speed and position of the particle according to the individual optimal position and the global optimal position;

[0144] V i (T+1)=ωV i (T)+c1×rand()×(pbest i -X i (T)+c2×rand()×(gbest-X i (T)))(10);

[0145] X i (T+1)=X i (T)+V i (T+1)(11);

[0146] Among them, V i (T+1) represents the velocity vector of the i-th particle at time T+1; pbest i represents the best historical position of the i-th particle; gbest represents the best historical position of the particle group; X i (T+1) represents the position vector at time T+1; V i (T+1) represents the velocity vector of the i-th particle at time T+1;

[0147] Step S36: Update the individual best position and the global best position;

[0148]

[0149] Where f(X) represents the fitness function; neighborhood (Xi) represents the position set of other particles in the neighborhood of the i-th particle;

[0150] Step S37: Repeat steps S33-S36 until the maximum number of iterations (100 times) is reached;

[0151] Step S38: Output the optimal hyperparameter C * and γ * , and train the final SVM model.

[0152] S4. Based on the user's specific needs, the trained biological functional element classification model is used to search and match in the database, to mine candidate biological functional elements that meet the requirements, and the optimal solution is recommended based on the sorting algorithm.

[0153] S41, using the biological function element classification model trained in step S3, combined with the biological function element feature vector extracted in step S2, to search and match in the constructed database;

[0154] S42, using association rules to search for biological components that meet user needs, the specific operation is as follows (such as Figure 4 shown):

[0155] S421, processing the biological function element feature vector and biological function description extracted in step S2 to obtain a data format suitable for an association rule mining algorithm;

[0156] S422, generating frequent itemsets;

[0157] Frequent itemsets = {itemsets|support(itemsets) ≥ minimum support} (13);

[0158] Among them, the item set represents the combination of the biological function element feature vector and the biological function description extracted from the database; the support (item set) represents the proportion of the item set in all records; the minimum support is a user-defined threshold used to determine whether the item set is frequent;

[0159] S423: Generate association rules;

[0160]

[0161] The antecedent represents the premise of the rule, i.e., a combination of one or more biological function element characteristics; the posterior represents the result of the rule, i.e., the biological function element characteristics associated with the antecedent.

[0162] S424: Calculate the confidence and lift, and obtain the filtered rules;

[0163]

[0164] Among them, confidence represents the probability of the occurrence of the latter under the condition that the former occurs; support (former ∪ latter) represents the support for the simultaneous occurrence of the former and the latter; support (former) represents the support for the occurrence of the former alone.

[0165]

[0166] Among them, lift represents the improvement of the support of the former for the latter relative to random selection; support (latter) represents the support of the latter when it appears alone;

[0167] Screening criteria:

[0168] Minimum confidence: A user-defined threshold used to determine whether a rule is strong. A rule is considered meaningful only if its confidence is above this threshold.

[0169] Minimum lift: A user-defined threshold used to further filter out rules with substantial impact. A lift greater than 1 indicates that the preceding item has a positive impact on the subsequent item, a lift equal to 1 indicates no impact, and a lift less than 1 indicates a negative impact

[0170] Screening Process:

[0171] Calculate confidence and lift: For each generated association rule, calculate its confidence and lift.

[0172] Filter rules: Filter out rules that meet the conditions based on the minimum confidence and minimum lift defined by the user.

[0173] Result output: The meaningful rules screened out are used for subsequent analysis or application.

[0174] S425, assigning an initial score to each candidate biological function element, the score being based on the confidence and lift of its association rule;

[0175] Rating = confidence × lift (17);

[0176] S426. Apply the filtered rules to search for the customer's specific needs, and find biological components that meet the user's needs.

[0177] S43. After mining multiple candidate biological function elements, use a ranking algorithm based on a gradient boosting tree to rank these candidate biological function elements to obtain the optimal biological function element. The specific operation is as follows:

[0178] S431, using the biological function element feature vector extracted in step S2 as an input feature and the score of the candidate biological function element obtained in step S425 as a target variable to train a gradient boosting tree model;

[0179] S432, using the test set of step S2 to verify the gradient boosting tree model and evaluate the prediction performance of the model;

[0180] S433, applying the trained gradient boosting tree model to the candidate biological function elements screened in step S42, and ranking all candidate biological function elements according to the scores predicted by the model;

[0181] S434, selecting the element with the highest score from the ranked candidate biological function element list as the optimal biological function element;

[0182] S435 : Output the sorted candidate biological function element list and the optimal biological function element to the user.

[0183] S44. Conduct experimental verification of the optimal biological functional element to confirm its actual function and performance;

[0184] S45, feeding back the verification results into the biological functional element classification model to update and optimize the model;

[0185] S46. Output the optimized model results to the user.

[0186] Example 2

[0187] A synthetic biology functional element mining system, comprising:

[0188] The database construction module is used to collect multi-source heterogeneous data, including gene sequences, protein structures, and biological function descriptions, pre-process the multi-source heterogeneous data, and build a database;

[0189] A feature extraction module is used to extract features from the processed data based on the GCNv2 feature extraction algorithm, generate biological function element feature vectors, and divide the biological function element feature vectors into a training set and a test set;

[0190] Model training and optimization module, which is used to introduce an improved particle swarm optimization algorithm to optimize the support vector machine and build and train a biological functional element classification model based on the training set and test set;

[0191] The functional element mining and recommendation module is used to search and match in the database based on the user's specific needs using the trained biological functional element classification model, to mine candidate biological functional elements that meet the requirements, and to recommend the optimal solution based on the sorting algorithm.

[0192] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0193] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0194] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0195] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0196] Therefore, the present invention adopts the above-mentioned synthetic biology functional element mining method, system, electronic equipment and medium to improve the efficiency and accuracy of synthetic biology functional element mining.

[0197] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for mining functional elements of synthetic biology, characterized in that: The following steps are involved: S1. Collect multi-source heterogeneous data, including gene sequences, protein structures, and biological function descriptions, pre-process the multi-source heterogeneous data, and build a database; S2. Perform feature extraction on the processed data based on the GCNv2 feature extraction algorithm to generate biological function element feature vectors, and divide the biological function element feature vectors into a training set and a test set; S3, introducing an improved particle swarm optimization algorithm to optimize the support vector machine, and constructing and training a biological functional element classification model based on the training set and test set in step S2; S4. Based on the user's specific needs, the trained biological functional element classification model is used to search and match in the database, identify candidate biological functional elements that meet the requirements, and recommend the optimal solution based on the ranking algorithm. The specific operations are as follows: S41, using the biological function element classification model trained in step S3, combined with the biological function element feature vector extracted in step S2, to search and match in the constructed database; S42, searching for biological components that meet user requirements using association rules; S43, after mining a plurality of candidate biological function elements, ranking the candidate biological function elements using a ranking algorithm based on a gradient boosting tree to obtain an optimal biological function element; S44. Conduct experimental verification of the optimal biological functional element to confirm its actual function and performance; S45, feeding back the verification results into the biological functional element classification model to update and optimize the model; S46. Output the optimized model results to the user.

2. A method for mining functional elements of synthetic biology according to claim 1, characterized in that: In step S1, the protein structure includes primary structure, secondary structure, tertiary structure and quaternary structure; the biological function description includes molecular function, biological process and cellular component.

3. The method for mining functional elements of synthetic biology according to claim 2, characterized in that: In step S2, feature extraction is performed on the processed data based on the GCNv2 feature extraction algorithm to generate a biological function element feature vector. The specific operation is as follows: S21. Input the preprocessed data into the GCNv2 network and use the convolutional layer to extract features from the preprocessed data. f (l+1) =σ(W (l) *f (l) +b (l) )(1); Among them, f (l) Represents the input feature map of the lth layer; W (l) represents the weight matrix of the lth layer; b (l) represents the bias vector of the lth layer; σ represents the ReLU activation function; * represents the convolution operation; f (l+1) Represents the input feature map of the l+1th layer; S22, using the output of step S21 (l+1) Generate probability maps and processed feature maps; Use the softmax function to transform f (l+1) Convert to a probability plot: o=softmax(f (l+1) )(2); Among them, o represents the probability map; f (l+1) Perform pooling and normalization operations to obtain the processed feature map f; S23, upsampling the probability map to match the resolution of the original image; output=PixeShuffle(o,r)(3); Where r represents the upsampling factor; output represents the probability map after upsampling; PixeShuffle represents the upsampling function; S24, performing non-maximum suppression on the upsampled probability map to determine the location of the feature points; S25, using the determined feature point positions, performing bilinear sampling on the processed feature map f obtained in step S22 to extract feature vectors corresponding to the feature point positions; desc=BilinearSampling(f,KL)(4); Where desc represents the feature vector after bilinear sampling; KL represents the position of the feature point; S26, converting the feature vector extracted in step S25 into a binary descriptor through a binarization network layer; desc'=κ(desc)(5); Where desc' represents the binary descriptor feature vector; κ represents the binarization function.

4. A method for mining functional elements of synthetic biology according to claim 3, characterized in that: In step S3, an improved particle swarm optimization algorithm is introduced to optimize the support vector machine. The specific operations are as follows: S31. Initialize the position X of each particle in the particle swarm i and speed V i , where i represents the index of the particle and the position represents the hyperparameters C and γ of the SVM; Position initialization: X i =[C i ,c i ](6); Speed initialization: V i =[vC i ,vγ i ](7); Among them, C i represents the hyperparameter C corresponding to the i-th particle; γ i represents the hyperparameter γ corresponding to the i-th particle; vC i represents the velocity component of the i-th particle corresponding to the hyperparameter C; vγ i represents the velocity component of the i-th particle corresponding to the hyperparameter γ; S32, define the fitness function, f(X i )=Accuracy(SVM(X i )), where Accuracy is the use of X i The classification accuracy of the trained SVM model; S33, dynamically adjust the inertia weight to balance global search and local search capabilities; Among them, ω represents the inertia weight; ω max Indicates the maximum value of inertia weight; ω min Indicates the minimum value of the inertia weight; t indicates the current number of iterations; iter m Indicates the maximum number of iterations; Step S34: dynamically adjust the learning factors c1 and c2 according to the relationship between the particle's historical best position and the global best position; Where c represents the learning factor c1 or c2; c max Indicates the maximum value of the learning factor c1 or c2; c min Indicates the minimum value of the learning factor c1 or c2; Step S35: updating the speed and position of the particle according to the individual optimal position and the global optimal position; V i (T+1)=ωV i (T)+c1×rand()×(pbest i -X i (T)+c2×rand()×(gbest-X i (T)))(10); X i (T+1)=X i (T)+V i (T+1)(11); Among them, V i (T+1) represents the velocity vector of the i-th particle at time T+1; pbest i represents the best historical position of the i-th particle; gbest represents the best historical position of the particle group; X i (T+1) represents the position vector at time T+1; V i (T+1) represents the velocity vector of the i-th particle at time T+1; Step S36: Update the individual best position and the global best position; Where f(X) represents the fitness function; neighborhood (Xi) represents the position set of other particles in the neighborhood of the i-th particle; Step S37: repeat steps S33-S36 until the maximum number of iterations is reached; Step S38: Output the optimal hyperparameter C * and γ * , and train the final SVM model.

5. The method for mining functional elements of synthetic biology according to claim 1, characterized in that: In step S42, the association rules are used to search for biological components that meet the user's requirements. The specific operations are as follows: S421, processing the biological function element feature vector and biological function description extracted in step S2 to obtain a data format suitable for an association rule mining algorithm; S422, generating frequent itemsets; Frequent itemsets = {itemsets | support(itemsets) ≥ minimum support} (13); Among them, the item set represents the combination of the biological function element feature vector and the biological function description extracted from the database; the support (item set) represents the proportion of the item set in all records; the minimum support is a user-defined threshold used to determine whether the item set is frequent; S423: Generate association rules; The antecedent represents the premise of the rule, i.e., a combination of one or more biological function element characteristics; the posterior represents the result of the rule, i.e., the biological function element characteristics associated with the antecedent. S424: Calculate the confidence and lift, and obtain the filtered rules; Among them, confidence represents the probability of the occurrence of the latter under the condition that the former occurs; support (former ∪ latter) represents the support for the simultaneous occurrence of the former and the latter; support (former) represents the support for the occurrence of the former alone. Among them, lift represents the improvement of the support of the former for the latter relative to random selection; support (latter) represents the support of the latter when it appears alone; S425, assigning an initial score to each candidate biological function element, the score being based on the confidence and lift of its association rule; Rating = confidence × lift (17); S426. Apply the filtered rules to searches based on customer-specific needs to mine candidate biological functional elements.

6. A method for mining functional elements of synthetic biology according to claim 5, characterized in that: In step S43, after mining a plurality of candidate biological function elements, a ranking algorithm based on a gradient boosting tree is used to sort these candidate biological function elements to obtain the optimal biological function element. The specific operation is as follows: S431, using the biological function element feature vector extracted in step S2 as an input feature and the score of the candidate biological function element obtained in step S425 as a target variable to train a gradient boosting tree model; S432, using the test set of step S2 to verify the gradient boosting tree model and evaluate the prediction performance of the model; S433, applying the trained gradient boosting tree model to the candidate biological function elements screened in step S42, and ranking all candidate biological function elements according to the scores predicted by the model; S434, selecting the element with the highest score from the ranked candidate biological function element list as the optimal biological function element; S435 : Output the sorted candidate biological function element list and the optimal biological function element to the user.

7. A synthetic biology functional element mining system, characterized in that: A method for mining a synthetic biology functional element according to any one of claims 1 to 6, comprising: The database construction module is used to collect multi-source heterogeneous data, including gene sequences, protein structures, and biological function descriptions, pre-process the multi-source heterogeneous data, and build a database; A feature extraction module is used to extract features from the processed data based on the GCNv2 feature extraction algorithm, generate biological function element feature vectors, and divide the biological function element feature vectors into a training set and a test set; Model training and optimization module, which is used to introduce an improved particle swarm optimization algorithm to optimize the support vector machine and build and train a biological functional element classification model based on the training set and test set; The functional element mining and recommendation module is used to search and match in the database based on the user's specific needs using the trained biological functional element classification model, to mine candidate biological functional elements that meet the requirements, and to recommend the optimal solution based on the sorting algorithm.

8. A computer device comprising: memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, the steps of the synthetic biology functional element mining method described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the synthetic biology functional element mining method according to any one of claims 1 to 6 are implemented.

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