A gene recognition method based on mask graph autoencoder

Abstract

The present invention discloses a gene identification method based on a masked graph autoencoder, which comprises the following steps: 1. obtaining gene interaction data and omics feature data and performing preprocessing; 2. masking the processed data, specifically including two branches, a node masking module and an edge masking module; 3. inputting the masked network into a graph autoencoder for training, which learns the network embedding representation by reconstructing the nodes and edges of the network; 4. obtaining a low-dimensional embedding of features through the trained encoder, and finally using a logistic regression classifier to classify genes. The present invention simultaneously focuses on the node information and structural information of the graph by masking the nodes and edges in the network respectively, and reduces the dependence of the feature training model on label information in a self-supervised learning manner, thereby accurately classifying genes.

Description

Technical Field

[0001] The present invention belongs to the field of gene recognition, and in particular to a gene recognition method based on a mask graph autoencoder. Background Art

[0002] With the rapid development of high-throughput sequencing technology, the field of genomics has accumulated massive amounts of data. This data, including genome sequences, transcriptome data, and proteome data, is highly complex and multidimensional. While this surge in data provides a rich resource for gene identification research, it also poses severe challenges to traditional data analysis methods.

[0003] The rapid development of artificial intelligence (AI) technology has provided new insights and methods for gene identification research. AI algorithms possess powerful data processing and pattern recognition capabilities, enabling them to automatically extract features from large datasets and accurately identify genes. Accurate gene identification can provide strong support for early disease diagnosis, personalized treatment, and drug development. This not only improves treatment outcomes and patient quality of life, but also drives innovation and development in the healthcare industry. Currently, algorithms for gene identification primarily fall into two categories: traditional machine learning-based methods and deep learning-based methods.

[0004] Traditional machine learning-based gene identification methods typically train a classifier, such as random forests and support vector machines, to identify genes by extracting different features from different types of omics data. However, most existing machine learning-based methods only extract network-based features through network analysis and cannot effectively combine the network topology characteristics of genes with multi-omics features.

[0005] In recent years, deep learning models have also achieved remarkable results in the field of gene identification. Some researchers have applied graph convolutional networks to gene identification tasks. For example, the Explainable Multi-Omics Graph Integration (EMOGI) method uses multiple types of omics features as gene features and uses graph convolutional networks to naturally combine gene networks with gene features to learn node embedding representations. However, the model performance of such methods mainly relies on a limited amount of labeled data, which results in the feature information of a large number of unlabeled nodes not being fully utilized, making it difficult to accurately identify genes. Summary of the Invention

[0006] In order to address the shortcomings of the above-mentioned existing technologies, the present invention proposes a gene identification method based on a mask graph autoencoder, in order to effectively combine the network characteristics and multi-omics characteristics of genes in the gene identification task, and make full use of the information of a large number of unlabeled nodes, thereby reducing the model's dependence on label data, and providing a new solution for gene identification.

[0007] In order to achieve the above-mentioned object, the present invention adopts the following technical solutions:

[0008] The gene identification method based on the mask image autoencoder of the present invention is characterized in that it is performed according to the following steps:

[0009] Step 1: Collect multi-omics data of normal genes and abnormal genes, and establish a gene network G={V, A, X}, where V represents the set of gene nodes and A represents the adjacency matrix of gene nodes. Represents the feature set of gene nodes, N is the number of gene nodes, M represents the number of omics types of each gene node feature; let any i-th gene node be denoted as v i , let x i Represents the i-th gene node v i characteristics, and ; Let e i,j Indicates v i With the jth gene v j If the interaction between i,j =1, indicating v i With v j There is an interaction relationship between them. If e i,j =0, indicating v i With v j There is no interaction between them; let the i-th gene node v i The true label is y i ,y i ∈{0,1}, 0 represents v i is a normal gene, 1 means v i For abnormal genes;

[0010] Step 2: Construct a graph representation learning network based on mask graph autoencoder, including: graph mask module, encoder, and decoder;

[0011] Step 2.1, the graph masking module is divided into a node masking unit and an edge masking unit, wherein the node masking unit performs node masking on V to obtain a node mask graph G1, and the edge masking unit performs edge masking on A to obtain an edge mask graph G2;

[0012] Step 2.2: The encoder module consists of L layers of GCN layers, and processes G1 and G2 respectively to obtain node feature representations accordingly. and ;

[0013] Step 2.3, the decoder module includes: node decoder unit and edge decoder unit, and respectively and Processing is performed and the reconstructed feature vector z is obtained accordingly. i and G2 in vi and v j The probability P(v i ,v j );

[0014] Step 2.4: Use formula (5) to construct the total loss function Loss;

[0015] (5)

[0016] In formula (5), loss1 is the node reconstruction loss, loss2 is the edge reconstruction loss, α represents the hyperparameter used to balance the node reconstruction and edge reconstruction loss functions, and 0<α<1;

[0017] Step 3: Use the Adam optimizer to train the weight parameters of the graph representation learning network, calculate the total loss function Loss to update the network parameters, and stop training when the number of iterations reaches the maximum number of iterations or the Loss reaches the minimum, thereby obtaining the optimal graph autoencoder model after training;

[0018] Step 4: Input G into the encoder of the trained optimal graph autoencoder model and generate a low-dimensional embedding feature S; let s i Represents the i-th gene node v in S i Low-dimensional embedding feature representation of ;

[0019] Step 5: Construct a logistic regression classifier by a fully connected layer, and use formula (10) to transform s i Project from feature space to probability space and output the i-th gene node v i Probability of abnormal gene ;

[0020] (10)

[0021] In formula (10), ω is the weight parameter to be learned in the fully connected layer, b is the bias term to be learned, and T represents the transpose;

[0022] Step 6: Use formula (11) to construct the cross entropy loss function L BCE , and use the gradient descent algorithm and Adam optimizer to train the logistic regression classifier so that the cross entropy loss function L BCE Convergence is achieved, thus obtaining a trained normal and abnormal gene classification model, which is used to classify normal and abnormal genes:

[0023] (11)

[0024] In formula (11), n ​​represents the number of gene nodes with labels, Represents the i-th gene node vi The true label.

[0025] The gene identification method based on the mask image autoencoder of the present invention is also characterized in that step 2.1 is performed as follows:

[0026] Step 2.1.1: The node mask unit samples V according to the mask ratio p and Bernoulli distribution to obtain the gene node subset V to be masked. mask , where 0 <p<1;

[0027] If v i The subset V of gene nodes to be masked mask , then v i The eigenvector x i Assigned to all zero vector, otherwise, v i The characteristics of x i Remain unchanged, thus obtaining the feature set X of the gene node after the mask operation mask , and then get the node mask graph G1={V, A, X mask};

[0028] Step 2.1.2: The edge mask unit samples V according to the sampling ratio q and Bernoulli distribution to obtain the root node set V root , where 0 <q<1;

[0029] V root As the starting point, use the random walk algorithm to find V root Sampling is performed, and a set of edges generated by random walk is used as the edge set to be masked, denoted as E mask ;

[0030] If e i,j The set of edges to be masked E mask , then set the element value of the i-th row and j-th column in the adjacency matrix A to "0", otherwise, set the element value of the i-th row and j-th column in the adjacency matrix A to "1", thus obtaining the adjacency matrix A after the mask operation mask ; Then we get the edge mask graph G2={V, A mask , X}.

[0031] Further, the step 2.2 is performed as follows:

[0032] Step 2.2.1, the node mask graph G1={V, A, X mask} is input into the encoder module, and the embedded feature representation of the node mask graph G1 output by the lth GCN layer is obtained using formula (1) :

[0033] (1)

[0034] In formula (1), represents the weight matrix to be learned in the first layer of GCN, σ(·) represents the nonlinear ReLU activation function; D represents the diagonal matrix, A represents the adjacency matrix of the gene node, I represents the identity matrix, Represents the node feature representation of the node mask graph G1 output by the lth GCN layer. When l=1, let = ;

[0035] Step 2.2.2: transform the edge mask graph G2={V, A mask , X} is input into the encoder module, and the edge mask graph G2 is obtained by using formula (2) after the embedding feature representation output by the lth GCN layer :

[0036] (2)

[0037] In formula (2), Represents the embedded feature representation of the edge mask graph G2 output by the lth GCN layer. When l=1, let = ;

[0038] Step 2.2.3: According to the process of step 2.2.1 and step 2.2.2, the node mask graph G1 is obtained respectively, and the node feature representation output by the L-layer GCN layer is obtained. The embedded feature representation of the edge mask graph G2 output by the L-layer GCN layer .

[0039] Further, step 2.3 is performed as follows:

[0040] Step 2.3.1, Input into the node decoder for processing, and use formula (3) to obtain the i-th gene node v in the node mask graph G1 i The reconstructed eigenvector z of i ;

[0041] (3)

[0042] In formula (3), MLP represents multi-layer perceptron, represents the i-th gene v in G1 i Embedded feature representation, h i ∈ ;

[0043] Step 2.3.2, Input it into the edge decoder for processing, and use formula (4) to obtain the node mask graph G2 v i and v j The probability P(v i ,v j );

[0044] (4)

[0045] In formula (4), represents the i-th gene v in G2 i The embedded feature representation of represents the jth gene v in G2 j Embedded feature representation of , ∈ , ⊙ represents the multiplication of corresponding elements, and sigmoid is a nonlinear activation function.

[0046] Further, step 2.4 is performed as follows:

[0047] Step 2.4.1, use formula (6) to construct the node reconstruction loss function loss1;

[0048] (6)

[0049] In formula (5), γ is the scaling factor, and γ ≥ 1;

[0050] Step 2.4.2: Use equations (7) to (9) to construct the edge reconstruction loss function loss2;

[0051] (7)

[0052] (8)

[0053] (9)

[0054] In formula (7) to formula (9), is a randomly generated set of edges that do not exist in the graph G; Indicates e i,j = 1, the difference between the predicted probability distribution and the probability distribution of the true label, Indicates e i,j = 0, the difference between the predicted probability distribution and the probability distribution of the true label.

[0055] The electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the gene identification method, and the processor is configured to execute the program stored in the memory.

[0056] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program executes the steps of the gene identification method when the computer program is executed by a processor.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. The present invention designs a network architecture of a graph autoencoder, which effectively combines the network characteristics of genes with multi-omics features. Compared with existing gene recognition methods, this method can better integrate gene features from different omics, thereby being more conducive to improving the accuracy of gene recognition.

[0059] 2. This paper designs an encoder module composed of a graph convolutional network (GCN). During the encoding process, the graph convolutional network is used to learn the graph representation of the gene network. Compared with existing gene recognition methods, the representation ability of embedded features is enhanced, thereby improving the accuracy of gene recognition.

[0060] 3. The method proposed in the present invention divides the mask module into node mask units and edge mask units through a dual-mask architecture, and simultaneously masks and reconstructs gene node features and topological connections, which enables the model to simultaneously consider node information and structural information in the gene network, thereby improving the accuracy of gene identification.

[0061] 4. The method proposed in the present invention performs feature learning in a self-supervised learning manner, ensuring that the model can fully utilize the information of a large number of unlabeled nodes in the gene network. Compared with existing gene identification methods, it avoids the model's dependence on label information during the feature learning process. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A structural diagram of the network in the present invention;

[0063] Figure 2 A diagram of the data processing process used in the present invention. DETAILED DESCRIPTION

[0064] In this embodiment, a gene identification method based on masked graph autoencoders mainly uses a graph autoencoder that masks both nodes and edges to mask and reconstruct nodes and edges in a gene interaction network. Then, an embedded representation is obtained through a trained GNN encoder, and the embedded representation is fed into a logistic regression classifier to complete gene identification. Figure 1 As shown, the specific steps of this method are as follows:

[0065] Step 1: Collect multi-omics data of normal and abnormal genes, and establish a gene network \(G = \{V, A, X\}\), where \(V\) represents the set of gene nodes, \(A\) represents the adjacency matrix of gene nodes, represents the set of features of gene nodes, \(N\) is the number of gene nodes, and \(M\) represents the number of omics types of each gene node feature; let any \(i\)-th gene node be denoted as \(v i _, and let \(x i represent the feature of the \(i\)-th gene node \(v i _, and ; let \(e i,j represent the interaction between \(v i and the \(j\)-th gene \(v j ; if \(e i,j = 1\), it means there is an interaction relationship between \(v i and \(v j ; if \(e i,j = 0\), it means there is no interaction relationship between \(v i and \(v j ; let the true label of the \(i\)-th gene node \(v i be \(y i _, \(y i \in\{0, 1\}\), \(0\) means \(v i is a normal gene, and \(1\) means \(v i is an abnormal gene; the gene interaction network data used in this embodiment is the high-confidence interaction network in the Consensus Path DB database, including 14,092 nodes and 3,448,62 edges; the gene features are 4 types of omics feature data from 3,318 samples, including copy number variation features, gene mutation features, DNA methylation features, and gene expression features. Therefore, \(N = 14,092\) and \(M = 4\). As Figure 2 shown, the 4 types of feature data from different omics are spliced to form a feature matrix, and then the feature matrix is combined with the gene interaction network into a gene network with multi-omics features.

[0066] Step 2: Construct a graph representation learning network based on a masked graph autoencoder, including: a graph masking module, an encoder, and a decoder;

[0067] Step 2.1: The graph masking module is divided into a node masking unit and an edge masking unit. Among them, the node masking unit performs node masking on \(V\) to obtain a node-masked graph \(G1\), and the edge masking unit performs edge masking on \(A\) to obtain an edge-masked graph \(G2\);

[0068] Step 2.1.1: The node masking unit samples \(V\) according to the masking ratio \(p\) and follows the Bernoulli distribution to obtain a subset \(V mask of gene nodes to be masked, where \(0 < p < 1\); in this embodiment, the masking ratio \(p = 0.5\);

[0069] If v i belongs to the subset V of gene nodes to be masked mask , then assign the feature vector x i of v i to the all-zero vector. Otherwise, the feature x i of v i remains unchanged, so as to obtain the feature set X of gene nodes after the masking operation mask , and further obtain the node masked graph G1 = {V, A, X mask};

[0070] Step 2.1.2: The edge masking unit samples V according to the sampling ratio q and follows the Bernoulli distribution to obtain the root node set V root , where 0 < q < 1; in this embodiment, the sampling ratio q = 0.6;

[0071] Taking any root node in V root as the starting point, use the random walk algorithm to sample V root , and take a group of edges generated by the random walk as the set of edges to be masked, denoted as E mask ;

[0072] If e i,j belongs to the set of edges to be masked E mask , then set the element value of the i-th row and j-th column in the adjacency matrix A to "0". Otherwise, set the element value of the i-th row and j-th column in the adjacency matrix A to "1", so as to obtain the adjacency matrix A mask after the masking operation; and further obtain the edge masked graph G2 = {V, A mask , X}.

[0073] Step 2.2: The encoder module consists of L GCN layers; in this embodiment, the encoder uses 2 GNN layers, that is, L = 2, and each GNN layer is processed using the batch normalization function;

[0074] Step 2.2.1: Input the node masked graph G1 = {V, A, X mask} into the encoder module, and use Equation (1) to obtain the embedded feature representation of the node masked graph G1 output by the l-th GCN layer :

[0075] (1)

[0076] In Equation (1), represents the weight matrix to be learned in the l-th GCN layer, σ(·) represents the non-linear activation function; D represents the diagonal matrix, A represents the adjacency matrix of gene nodes, and I represents the identity matrix, Represents the node feature representation of the node mask graph G1 output by the lth GCN layer. When l=1, let = ; In this embodiment, the activation function σ(·) uses the ELU activation function;

[0077] Step 2.2.2: transform the edge mask graph G2={V, A mask , X} is input into the encoder module, and the edge mask graph G2 is obtained by using formula (2) after the embedding feature representation output by the lth GCN layer :

[0078] (2)

[0079] In formula (2), Represents the embedded feature representation of the edge mask graph G2 output by the lth GCN layer. When l=1, let = ;

[0080] Step 2.2.3: According to the process of step 2.2.1 and step 2.2.2, the node mask graph G1 is obtained respectively, and the node feature representation output by the L-layer GCN layer is obtained. The embedded feature representation of the edge mask graph G2 output by the L-layer GCN layer .

[0081] Step 2.3, the decoder module includes: a node decoder unit and an edge decoder unit;

[0082] Step 2.3.1, Input into the node decoder for processing, and use formula (3) to obtain the i-th gene node v in the node mask graph G1 i The reconstructed eigenvector z of i ;

[0083] (3)

[0084] In formula (3), MLP represents multi-layer perceptron, represents the i-th gene v in G1 i Embedded feature representation, h i ∈ ; In this embodiment, the multilayer perceptron MLP includes two layers of fully connected neural networks;

[0085] Step 2.3.2, Input it into the edge decoder for processing, and use formula (4) to obtain the node mask graph G2 v i and v j The probability P(v i ,vj );

[0086] (4)

[0087] In formula (4), represents the i-th gene v in G2 i The embedded feature representation of represents the jth gene v in G2 j Embedded feature representation of , ∈ , ⊙ represents the multiplication of corresponding elements, and sigmoid is a nonlinear activation function.

[0088] Step 2.4: Use formula (5) to construct the total loss function Loss;

[0089] (5)

[0090] In formula (5), loss1 is the node reconstruction loss, loss2 is the edge reconstruction loss, α represents the hyperparameter used to balance the node reconstruction and edge reconstruction loss functions, and 0<α<1; in this embodiment, the hyperparameter α is 0.4;

[0091] Step 2.4.1, use formula (6) to construct the node reconstruction loss function loss1;

[0092] (6)

[0093] In formula (5), γ is a scaling factor, and γ≥1; in this embodiment, the scaling factor γ is 3;

[0094] Step 2.4.2: Use equations (7) to (9) to construct the edge reconstruction loss function loss2;

[0095] (7)

[0096] (8)

[0097] (9)

[0098] In formula (7) to formula (9), is a randomly generated set of edges that do not exist in the graph G; Indicates that when e i,j = 1, the difference between the predicted probability distribution and the probability distribution of the true label, Indicates that when e i,j = 0, the difference between the predicted probability distribution and the probability distribution of the true label.

[0099] Step 3. Use the Adam optimizer to train the weight parameters of the graph representation learning network, calculate the total loss function Loss to update the network parameters, and stop training when the number of iterations reaches the maximum number of iterations or when the Loss reaches the minimum, thereby obtaining the optimal graph autoencoder model after training; in this embodiment, the node reconstruction task and the edge reconstruction task are trained simultaneously, and the training is terminated after 50 training iterations. The initial learning rate and weight decay are set to 0.01 and 0.0005, respectively.

[0100] Step 4: Input G into the encoder of the trained optimal graph autoencoder model and generate a low-dimensional embedding feature S; let s i Represents the i-th gene node v in S i Low-dimensional embedding feature representation of ;

[0101] Step 5: Construct a logistic regression classifier by a fully connected layer, and use formula (10) to transform s i Project from feature space to probability space and output the i-th gene node v i Probability of abnormal gene ;

[0102] (10)

[0103] In formula (10), ω is the weight parameter to be learned in the fully connected layer, b is the bias term to be learned, and T represents the transpose.

[0104] Step 6: Use formula (11) to construct the cross entropy loss function L BCE , and use the gradient descent algorithm and Adam optimizer to train the logistic regression classifier so that the cross entropy loss function L BCE Convergence is achieved, thereby obtaining a trained normal and abnormal gene classification model for classifying normal and abnormal genes. In this embodiment, 20% of all labeled genes are used as a test set and 80% as a training set. The logistic regression classifier is trained for 200 epochs, and the learning rate is set to 0.001.

[0105] (11)

[0106] In formula (11), n ​​represents the number of gene nodes with labels, Represents the i-th gene node v i The true label.

[0107] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0108] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are executed.

Claims

1. A gene recognition method based on mask graph autoencoder, characterized in that: The steps are as follows: Step 1: Collect multi-omics data of normal genes and abnormal genes, and establish a gene network G={V, A, X}, where V represents the set of gene nodes and A represents the adjacency matrix of gene nodes. Represents the feature set of gene nodes, N is the number of gene nodes, M represents the number of omics types of each gene node feature; let any i-th gene node be denoted as v i , let x i Represents the i-th gene node v i characteristics, and ; Let e i,j Indicates v i With the jth gene v j If the interaction between i,j =1, indicating v i With v j There is an interaction relationship between them. If e i,j =0, indicating v i With v j There is no interaction between them; let the i-th gene node v i The true label is y i ,y i ∈{0,1}, 0 represents v i is a normal gene, 1 means v i For abnormal genes; Step 2: Construct a graph representation learning network based on mask graph autoencoder, including: graph mask module, encoder, and decoder; Step 2.1, the graph masking module is divided into a node masking unit and an edge masking unit, wherein the node masking unit performs node masking on V to obtain a node mask graph G1, and the edge masking unit performs edge masking on A to obtain an edge mask graph G2; Step 2.2: The encoder module consists of L layers of GCN layers, and processes G1 and G2 respectively to obtain node feature representations accordingly. and ; Step 2.3, the decoder module includes: node decoder unit and edge decoder unit, and respectively and Processing is performed and the reconstructed feature vector z is obtained accordingly. i and G2 in v i and v j The probability P(v i ,v j ); Step 2.4: Use formula (5) to construct the total loss function Loss; (5) In formula (5), loss1 is the node reconstruction loss, loss2 is the edge reconstruction loss, α represents the hyperparameter used to balance the node reconstruction and edge reconstruction loss functions, and 0<α<1; Step 3: Use the Adam optimizer to train the weight parameters of the graph representation learning network, calculate the total loss function Loss to update the network parameters, and stop training when the number of iterations reaches the maximum number of iterations or the Loss reaches the minimum, thereby obtaining the optimal graph autoencoder model after training; Step 4: Input G into the encoder of the trained optimal graph autoencoder model and generate a low-dimensional embedding feature S; let s i Represents the i-th gene node v in S i Low-dimensional embedding feature representation of ; Step 5: Construct a logistic regression classifier by a fully connected layer, and use formula (10) to transform s i Project from feature space to probability space and output the i-th gene node v i Probability of abnormal gene ; (10) In formula (10), ω is the weight parameter to be learned in the fully connected layer, b is the bias term to be learned, and T represents the transpose; Step 6: Use formula (11) to construct the cross entropy loss function L BCE , and use the gradient descent algorithm and Adam optimizer to train the logistic regression classifier so that the cross entropy loss function L BCE Convergence is achieved, thus obtaining a trained normal and abnormal gene classification model, which is used to classify normal and abnormal genes: (11) In formula (11), n ​​represents the number of gene nodes with labels, Represents the i-th gene node v i The true label.

2. A gene recognition method based on mask image autoencoder according to claim 1, characterized in that: The step 2.1 is carried out as follows: Step 2.1.1: The node mask unit samples V according to the mask ratio p and Bernoulli distribution to obtain the gene node subset V to be masked. mask , where 0 <p<1; If v i The subset V of gene nodes to be masked mask , then v i The eigenvector x i Assigned to all zero vector, otherwise, v i The characteristics of x i Remain unchanged, thus obtaining the feature set X of the gene node after the mask operation mask , and then get the node mask graph G1={V, A, X mask }; Step 2.1.2: The edge mask unit samples V according to the sampling ratio q and Bernoulli distribution to obtain the root node set V root , where 0 <q<1; V root As the starting point, use the random walk algorithm to find V root Sampling is performed, and a set of edges generated by random walk is used as the edge set to be masked, denoted as E mask ; If e i,j The set of edges to be masked E mask , then set the element value of the i-th row and j-th column in the adjacency matrix A to "0", otherwise, set the element value of the i-th row and j-th column in the adjacency matrix A to "1", thus obtaining the adjacency matrix A after the mask operation mask ; Then we get the edge mask graph G2={V, A mask , X}.

3. A gene recognition method based on mask image autoencoder according to claim 2, characterized in that: The step 2.2 is carried out as follows: Step 2.2.1, the node mask graph G1={V, A, X mask } is input into the encoder module, and the embedded feature representation of the node mask graph G1 output by the lth GCN layer is obtained using formula (1) : (1) In formula (1), represents the weight matrix to be learned in the first layer of GCN, σ(·) represents the nonlinear ReLU activation function; D represents the diagonal matrix, A represents the adjacency matrix of the gene node, I represents the identity matrix, Represents the node feature representation of the node mask graph G1 output by the lth GCN layer. When l=1, let = ; Step 2.2.2: transform the edge mask graph G2={V, A mask , X} is input into the encoder module, and the edge mask graph G2 is obtained by using formula (2) after the embedding feature representation output by the lth GCN layer : (2) In formula (2), Represents the embedded feature representation of the edge mask graph G2 output by the lth GCN layer. When l=1, let = ; Step 2.2.3: According to the process of step 2.2.1 and step 2.2.2, the node mask graph G1 is obtained respectively, and the node feature representation output by the L-layer GCN layer is obtained. The embedded feature representation of the edge mask graph G2 output by the L-layer GCN layer .

4. A gene recognition method based on mask image autoencoder according to claim 3, characterized in that: Step 2.3 is performed as follows: Step 2.3.1, Input into the node decoder for processing, and use formula (3) to obtain the i-th gene node v in the node mask graph G1 i The reconstructed eigenvector z of i ; (3) In formula (3), MLP represents multi-layer perceptron, represents the i-th gene v in G1 i Embedded feature representation, h i ∈ ; Step 2.3.2, Input it into the edge decoder for processing, and use formula (4) to obtain the node mask graph G2 v i and v j The probability P(v i ,v j ); (4) In formula (4), represents the i-th gene v in G2 i The embedded feature representation of represents the jth gene v in G2 j Embedded feature representation of , ∈ , ⊙ represents the multiplication of corresponding elements, and sigmoid is a nonlinear activation function.

5. A gene recognition method based on mask image autoencoder according to claim 4, characterized in that: Step 2.4 is performed as follows: Step 2.4.1, use formula (6) to construct the node reconstruction loss function loss1; (6) In formula (5), γ is the scaling factor, and γ ≥ 1; Step 2.4.2: Use equations (7) to (9) to construct the edge reconstruction loss function loss2; (7) (8) (9) In formula (7) to formula (9), is a randomly generated set of edges that do not exist in the graph G; Indicates e i,j = 1, the difference between the predicted probability distribution and the probability distribution of the true label, Indicates e i,j = 0, the difference between the predicted probability distribution and the probability distribution of the true label.

6. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the gene identification method according to any one of claims 1 to 5, and the processor is configured to execute the program stored in the memory.

7. 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 gene identification method according to any one of claims 1 to 5 are executed.

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