A cancer classification detection method based on multi-omics data clustering and relabeling

Abstract

The present invention discloses a cancer category detection method based on multi-omics data clustering and re-labeling, which relates to the field of machine learning technology. The learning process of the category detection model is: obtaining n multi-omics data samples, each sample contains m different categories of omics data, and the sample label is the cancer category corresponding to the sample, with a total of K cancer categories; pre-processing the samples, and using the model to generate sample features for the pre-processed samples; clustering the pre-processed samples into K clusters based on the sample features; performing impurity measurement on each cluster, and re-labeling the sample labels within the cluster; performing comparative learning based on the re-labeling results, so that the model generates accurate sample features; and predicting the cancer category based on the accurate sample features generated by the model. The present invention uses the category detection model to perform cancer category detection, which is beneficial to improving the accuracy of cancer typing through the reconstruction of sample labels and the application of comparative learning methods.

Description

Technical Field

[0001] The present invention relates to the field of machine learning technology, and in particular to a method for detecting cancer categories based on multi-omics data clustering and re-labeling. Background Art

[0002] Cancer classification detection methods based on multi-omics data aim to analyze cancer samples using diverse biological information (such as DNA methylation, mRNA expression, and miRNA expression) to reveal biological differences between different subtypes. This approach can help us better understand cancer pathogenesis and prognosis, and guide personalized treatment. Existing multi-omics fusion algorithms primarily involve the following steps: First, each omics data set is normalized. Next, multiple omics data sets from the same sample are fused, followed by similarity metrics to construct a multi-omics similarity matrix. This similarity matrix calculation incorporates information from different omics data types, providing a more comprehensive reflection of the degree of association between samples. Next, similarity matrices from different data types are fused, for example by calculating the weighted average of each similarity matrix with the others to obtain an updated fused similarity matrix. Finally, clustering algorithms are used to perform cluster analysis on the fused similarity matrix to classify samples into distinct clusters or subtypes.

[0003] Cancer classification detection methods based on multi-omics data have the following disadvantages:

[0004] In actual research, model training relies on a large amount of manually labeled data. Due to the high degree of subjectivity and uncontrollability of the labeling process, manually labeled labels often have high uncertainty. This uncertainty has two drawbacks:

[0005] 1. Cancer samples of the same category will be assigned to different label categories. For example, cancer samples of the same subtype may have different labels if labeled by different labelers.

[0006] 2. There are cancer samples that cannot be classified into known label categories. Labelers can only label according to preset label categories. However, in the actual labeling process, there are often some samples that cannot be classified into known label categories.

[0007] In summary, label uncertainty has been one of the main factors that seriously affects the performance degradation of cancer category detection models. Summary of the Invention

[0008] In order to overcome the above-mentioned defects in the prior art, the present invention provides a cancer category detection method based on multi-omics data clustering and re-labeling, which is conducive to improving the accuracy of cancer classification through the reconstruction of sample labels and the application of comparative learning methods.

[0009] To achieve the above object, the present invention adopts the following technical solutions, including:

[0010] A cancer classification detection method based on multi-omics data clustering and relabeling uses a classification detection model to detect cancer classifications. The classification detection model learning process includes the following steps:

[0011] S1: Obtain n multi-omics data samples, each containing m different categories of omics data. The sample label is the cancer category corresponding to the sample, and there are K cancer categories in total. Preprocess the samples and use the model to generate sample features for the preprocessed samples.

[0012] S2, clustering the preprocessed samples into K clusters according to sample characteristics;

[0013] S3, impurity measurement for each cluster and relabeling of sample labels within the cluster;

[0014] S4, based on the re-labeling results, conduct comparative learning to enable the model to generate accurate sample features;

[0015] S5, predicts the cancer category based on the accurate sample features generated by the model.

[0016] Preferably, the specific process of step S3 is as follows:

[0017] The cluster label R(c) of cluster c is:

[0018]

[0019] in, represents the probability of the sample in cluster c being labeled y; y represents the sample label; Y c Represents the sample label set of all samples in cluster c, and the argmax function represents the The sample label y corresponding to the maximum value;

[0020] Impurity level Imp of cluster c c for:

[0021]

[0022] Where C represents the total number of clusters;

[0023] Set the impurity lower limit θ respectively low and the impurity upper limit θ high :

[0024] If Imp c <θ low, then the samples in cluster c whose sample labels are different from the cluster label R(c) are considered to be samples with initial incorrect labels. The samples with initial incorrect labels are relabeled, and the sample labels are relabeled to R(c).

[0025] If θ low <Imp c <θ high , then the samples in cluster c whose sample labels are different from the cluster label R(c) are considered to be noise samples, and the noise samples are relabeled, and the sample labels are relabeled as noise sample labels.

[0026] If Imp c >θ high , then all samples in cluster c are relabeled, and the sample label is relabeled as a new unknown sample label.

[0027] Preferably, the comparative learning in step S4 is specifically as follows:

[0028] After relabeling, samples whose sample labels are the same as the cluster labels are called accurate samples;

[0029] Randomly find two accurate samples and perform comparative learning on the two accurate samples. When the two accurate samples have the same sample label, that is, the same cluster label, minimize the distance between the two accurate samples; when the two accurate samples have different sample labels, that is, different cluster labels, maximize the distance between the two accurate samples;

[0030] Randomly find an accurate sample, compare the accurate sample with the noise sample, and maximize the distance between the accurate sample and the noise sample;

[0031] The distance refers to the distance between sample features.

[0032] Preferably, the formula for contrastive learning is:

[0033]

[0034] Among them, L represents the loss function of contrastive learning, and the purpose of contrastive learning is to minimize the loss function L; τ and q both represent coefficients; d + Indicates the distance between the positive sample pairs corresponding to the current accurate sample, d i Represents the distance between any pair of samples corresponding to the current accurate sample;

[0035] The current accurate sample and the accurate sample with the same sample label constitute the positive sample pair; the current accurate sample and all other accurate samples or noise samples constitute the arbitrary sample.

[0036] Preferably, the preprocessing method of step S1 is specifically as follows:

[0037] n multi-omics data samples are denoted as in, is the nth sample, l is the category index of the omics data, l = 1, 2, ..., m;

[0038] Normalize multi-omics data:

[0039]

[0040] in, is the normalized omics data of the lth category in the i-th sample, is the l-th type of omics data in the i-th sample, and Respectively The expectation and variance of

[0041] The normalized omics data of different categories of the same sample are spliced ​​and fused to obtain the normalized sample, that is, the preprocessed sample:

[0042]

[0043] in, It represents the i-th sample after normalization or preprocessing.

[0044] Preferably, in step S2, clustering is performed using a K-means clustering algorithm, as shown below:

[0045] S21, select K value: set the number of clusters to K, corresponding to K cancer categories;

[0046] S22, initialization: samples with the same sample label are initialized into a cluster, the initialization cluster label is the same as the sample label, and the mean of the samples with the same sample label is calculated as the center of the initialization cluster; the mean is calculated using the sample features;

[0047] S23, allocation: Calculate the distance between each sample and each cluster center respectively, and allocate each sample to the cluster corresponding to the nearest cluster center; the distance is calculated using the sample features and the cluster center features;

[0048] S24, update: recalculate the cluster center of each cluster according to the current cluster assignment;

[0049] S25, repeating steps S23 and S24 until the cluster center no longer changes or the specified maximum number of iterations is reached.

[0050] Preferably, in the category detection model, the preprocessed samples Sample feature X extracted using CNN networki ; Use the embedding layer to embed the preprocessed samples The sample feature pairs (X i ,X j ) is mapped into a feature space and the feature vector pair (E i ,E j ); Use Multi-Layer Perceptron (MLP) to predict cancer categories.

[0051] The present invention also provides a readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the above-mentioned method for detecting cancer categories based on multi-omics data clustering and re-labeling.

[0052] The present invention also provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements the above-mentioned cancer category detection method based on multi-omics data clustering and re-labeling.

[0053] The present invention also provides a computer program product, which includes a computer program / instruction, which, when executed by a processor, implements the above-mentioned cancer category detection method based on multi-omics data clustering and re-labeling.

[0054] The advantages of the present invention are:

[0055] (1) Through the reconstruction of sample labels and the application of contrastive learning methods, the model can learn strong discriminative features, which is conducive to improving the accuracy of cancer classification.

[0056] (2) In the clustering process, in addition to the manually labeled cancer sample labels, an additional cluster label (cluster label) is generated, and the sample labels of the samples are dynamically adjusted according to the clustering results. The corrected sample labels are then used to further guide the model's cancer category prediction process.

[0057] (3) The re-labeling method is introduced, which enables the model to better explore the intrinsic characteristics of the data itself during the clustering process and achieve more complex sample label reconstruction.

[0058] (4) Clustering, cluster impurity measurement, and relabeling can be used to subdivide the same cancer category and discover new subtypes. During the training process, some noisy samples may be assigned a sample label, but the clustering iteration process may reclassify the sample as a noise sample. In this case, cluster impurity measurement and relabeling can be used to discover the existence of a new cancer subtype. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1This is a flow chart of a cancer category detection method based on multi-omics data clustering and re-labeling according to the present invention.

[0060] Figure 2 Schematic diagram of a cancer category detection method based on multi-omics data clustering and re-labeling of the present invention.

[0061] Figure 3 Schematic diagram of comparative learning of the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0063] Example 1

[0064] Depend on Figure 1 and Figure 2 As shown, the present invention provides a method for detecting cancer categories based on multi-omics data clustering and re-labeling, comprising the following steps:

[0065] S1, multi-omics data preprocessing: normalize the collected multi-omics data, and perform multi-omics data fusion and feature extraction.

[0066] Given n omics samples, each sample contains m different types of omics data (such as DNA methylation, mRNA expression, miRNA expression, etc.); n samples are denoted as {x i |i=1,2,...,n}, Among them, x i is the i-th sample, and l is the category index of the omics data.

[0067] First, the multi-omics data is normalized and formalized as follows:

[0068]

[0069] in, is the normalized omics data of the lth category in the i-th sample, is the l-th type of omics data in the i-th sample, and Respectively The expectation and variance of .

[0070] Then, the normalized omics data of different categories of the same sample are spliced ​​and fused to obtain the normalized sample, which is formalized as follows:

[0071]

[0072] in, represents the normalized i-th sample.

[0073] The normalized samples Extract features through CNN network and get samples Features of X i ; Then the normalized sample pairs The feature representation X i and X j , mapped into a feature space through an embedding layer, and the corresponding feature vector pair (E i ,E j ).

[0074] S2, sample clustering: Use the K-means clustering algorithm to cluster the normalized samples and generate corresponding clustering scores.

[0075] Assume that there are K cancer categories initially, each sample has a sample label (cancer category), and the initial cluster label is the same as the sample label. Using the K-means algorithm, a cluster score is generated for each sample within the cluster. The cluster score reflects the distance between the sample and the cluster center.

[0076] Although each sample has a unique, human-labeled sample label (i.e., cancer category), low-quality or mislabeled noise labels are common due to human limitations. This application uses clustering methods to help eliminate or correct these low-quality labels and identify the true sample label (i.e., the true cancer category) corresponding to the sample, thereby improving label quality and, consequently, the accuracy of cancer category prediction.

[0077] The purpose of clustering in this application is to give samples an additional cluster label (i.e., cluster label) in addition to the sample label, and through a series of steps and algorithms, find samples with lower confidence based on the clustering results, and give the algorithm some ability to correct sample labels.

[0078] The K-means clustering algorithm clusters data points by minimizing the sum of the squared distances between each sample and the cluster center. The cluster center is the average of all points (samples) in the cluster and represents the center of the cluster. The K-means clustering algorithm converges gradually by continuously updating cluster assignments and cluster centers.

[0079] The process of the K-means clustering algorithm includes two main steps: allocation and update, as shown below:

[0080] S21, select K value: set the number of clusters.

[0081] S22, initialization: samples with the same sample label are initialized into a cluster, the initialized cluster label is the same as the sample label, and the mean of the samples with the same sample label is calculated as the initialized cluster center, with a total of K cluster centers; the mean is calculated using sample features.

[0082] S23, allocation: for each sample in the data set, respectively calculate the distance between each sample and each cluster center, and allocate each sample to the cluster corresponding to the nearest cluster center; the distance is calculated using the features of the sample and the features of the cluster center.

[0083] S24, Update: Recalculate the cluster center of each cluster according to the current cluster assignment, that is, calculate the mean of all samples in the cluster as the new cluster center.

[0084] S25, repeating step S23 and step S24, and continuously repeating the steps of allocating and updating until the cluster center no longer changes (ie, converges) or the specified maximum number of iterations is reached.

[0085] S3, Cluster Impurity Measurement and Relabeling: Cluster analysis is performed on omics data using a clustering algorithm. Samples with similar distances are grouped into the same cluster and corresponding sample and cluster labels are generated. Automatic relabeling is performed based on the corresponding cluster labels, modifying the sample labels corresponding to the samples.

[0086] Furthermore, you can check whether there are subclusters within cluster c. A subcluster refers to the presence of two subcluster centers that are far apart within the same cluster. That is, the distance between the two subcluster centers is greater than a set threshold, and the samples within the subcluster follow a Gaussian distribution. For example, if two subclusters c1 and c2 are found within cluster c using the K-means clustering algorithm, if the distance between the cluster centers of the two subclusters c1 and c2 is greater than the set threshold, then the two subclusters c1 and c2 exist within cluster c. Otherwise, the two subclusters c1 and c2 do not exist within cluster c.

[0087] When the distance between the samples in cluster c (or subclusters c1, c2) and the cluster center exceeds the preset threshold δ, they are judged as outlier samples (noise samples). Belongs to cluster c, and the sample The distance μ from the cluster center c When it is greater than the threshold δ, the sample is judged to be is a noise sample.

[0088] The impurity level of a cluster is an important metric that determines the relabeling strategy for samples within the cluster. If the impurity level is low, indicating that the cluster is primarily composed of samples from a single cancer category, the cluster is considered to be a representative group of that cancer category, and the cluster label is that cancer category. However, if the impurity level is high, indicating that the cluster is composed of samples from different cancer categories in similar proportions, the cluster is not representative of that cancer category. Noise samples are not considered in the cluster impurity measurement and relabeling phase. Noise samples within a cluster are assigned a new sample label, the noise sample label.

[0089] The cluster label R(c) of cluster c is expressed as:

[0090]

[0091] in, represents the probability of the sample in cluster c being labeled y; y represents the sample label; Y c Represents the sample label set of all samples in cluster c, and the argmax function represents the The sample label y corresponding to the maximum value.

[0092] Impurity level Imp of cluster c c The metrics are expressed as:

[0093]

[0094] Where C represents the total number of clusters.

[0095] Set the impurity lower limit θ respectively low and the impurity upper limit θ high , the impurity level of the cluster is divided into three levels, and different re-labeling strategies are adopted for clusters at each level:

[0096] If Imp c <θ low , then the impurity level of cluster c is low impurity, and the samples in cluster c whose sample labels are different from the cluster label R(c) are considered to be samples with initial incorrect labels, and the samples with initial incorrect labels are relabeled, and the sample labels are relabeled to R(c).

[0097] If θ low <Imp c <θ high , then the impurity level of cluster c is medium impurity. In order to minimize the error re-labeling, the samples in cluster c whose sample labels are different from the cluster label R(c) are re-labeled, and the sample labels are re-labeled as noise sample labels.

[0098] If Imp c >θ high, then the impurity level of cluster c is high impurity, all samples in cluster c are relabeled, and the sample label is relabeled as a new unknown sample label.

[0099] S4, contrastive learning: Based on the results of re-labeling, the sample features of accurately labeled sample labels and cluster labels are screened for contrastive learning to promote the model to generate more accurate sample features.

[0100] Depend on Figure 3 As shown, samples with the same sample label and cluster label are called accurate samples; two accurate samples are randomly found and compared and learned. When the two accurate samples have the same sample label, that is, the same cluster label (the two accurate samples are called positive sample pairs), the distance between the two accurate samples is minimized; when the two accurate samples have different sample labels, that is, different cluster labels (the two accurate samples are called negative sample pairs), the distance between the two accurate samples is maximized.

[0101] Randomly find an accurate sample, compare the accurate sample with the noise sample, and maximize the distance between the accurate sample and the noise sample.

[0102] The distance is calculated by using Euclidean distance or cosine distance to calculate the sample features.

[0103] The formula for contrastive learning is as follows:

[0104]

[0105] Among them, L represents the loss function of contrastive learning, and the purpose of contrastive learning is to minimize the loss function L; τ is the temperature coefficient of contrastive learning, which is set to 1 by default; q is the weight coefficient, which is set to 1 by default; d + Represents the distance between the positive sample pair corresponding to the current sample, and the current accurate sample and the accurate sample with the same sample label constitute the positive sample pair; d i Represents the distance between any pair of samples corresponding to the current sample. The current accurate sample and all other accurate samples or noise samples constitute the arbitrary sample; K represents the number of labels.

[0106] S5, Cancer Class Prediction: Predict the final cancer class based on the more accurate sample features generated by the model.

[0107] The more accurate sample features generated by the model are input into a classifier (such as a multi-layer perceptron MLP) to predict the category of cancer. The prediction process can be formalized as follows:

[0108] S = σ(MLP(X;θ MLP ))

[0109] Among them, σ is a nonlinear activation function, such as the softmax function, which maps the output of the classifier to the interval [0,1]. MLP represents the learnable weight parameters of the classifier, and X is the more accurate sample feature generated by the model.

[0110] Example 2

[0111] An electronic device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for detecting cancer categories based on multi-omics data clustering and relabeling according to the first embodiment is implemented.

[0112] The electronic device of the embodiment of the present application can be the mobile device itself, or a stand-alone device independent of the mobile device, which can communicate with the mobile device to receive the collected input signals from them and send the selected target decision behavior to them.

[0113] The electronic device includes one or more processors and memory. The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the decision-making behavior decision-making method and / or other desired functions of the various embodiments of the present application described above.

[0114] The electronic device may further include input means and output means.

[0115] Example 3

[0116] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps in the decision-making behavior decision-making method according to various embodiments of the present application described in the above-mentioned embodiment 1 of this specification.

[0117] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0118] Example 4

[0119] An embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, causes the processor to execute the steps of the decision-making behavior decision-making method according to various embodiments of the present application described in the above-mentioned embodiment 1.

[0120] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0121] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting cancer types based on multi-omics data clustering and re-labeling, characterized in that: Cancer classification is detected using a classification detection model. The learning process of the classification detection model includes the following steps: S1: Obtain n multi-omics data samples, each containing m different categories of omics data. The sample label is the cancer category corresponding to the sample, and there are K cancer categories in total. Preprocess the samples and use the model to generate sample features for the preprocessed samples. S2, clustering the preprocessed samples into K clusters according to sample characteristics; S3, impurity measurement for each cluster and relabeling of sample labels within the cluster; S4, based on the re-labeling results, conduct comparative learning to enable the model to generate accurate sample features; S5, predicts the cancer category based on the accurate sample features generated by the model; The specific process of step S3 is as follows: The cluster label R(c) of cluster c is: in, represents the probability of the sample in cluster c being labeled y; y represents the sample label; Y c Represents the sample label set of all samples in cluster c, and the argmax function represents the The sample label y corresponding to the maximum value; Impurity level Imp of cluster c c for: Where C represents the total number of clusters; Set the impurity lower limit θ respectively low and the impurity upper limit θ high : If Imp c <θ low , then the samples in cluster c whose sample labels are different from the cluster label R(c) are considered to be samples with initial incorrect labels. The samples with initial incorrect labels are relabeled, and the sample labels are relabeled to R(c); If θ low <Imp c <θ high , then the samples in cluster c whose sample labels are different from the cluster label R(c) are considered to be noise samples, and the noise samples are relabeled, and the sample labels are relabeled as noise sample labels; If Imp c >θ high , then all samples in cluster c are relabeled, and the sample label is relabeled as a new unknown sample label.

2. A method for detecting cancer types based on multi-omics data clustering and re-labeling according to claim 1, characterized in that: The contrastive learning in step S4 is specifically as follows: After relabeling, samples whose sample labels are the same as the cluster labels are called accurate samples; Randomly find two accurate samples and perform comparative learning on the two accurate samples. When the two accurate samples have the same sample label, that is, the same cluster label, minimize the distance between the two accurate samples; when the two accurate samples have different sample labels, that is, different cluster labels, maximize the distance between the two accurate samples; Randomly find an accurate sample, compare the accurate sample with the noise sample, and maximize the distance between the accurate sample and the noise sample; The distance refers to the distance between sample features.

3. The method for cancer classification detection based on multi-omics data clustering and re-labeling according to claim 2, characterized in that: The formula for contrastive learning is: Among them, L represents the loss function of contrastive learning, and the purpose of contrastive learning is to minimize the loss function L; τ and q both represent coefficients; d + Indicates the distance between the positive sample pairs corresponding to the current accurate sample, d i Represents the distance between any pair of samples corresponding to the current accurate sample; The current accurate sample and the accurate sample with the same sample label constitute the positive sample pair; the current accurate sample and all other accurate samples or noise samples constitute the arbitrary sample.

4. A method for detecting cancer types based on multi-omics data clustering and re-labeling according to any one of claims 1-2, characterized in that: The preprocessing method of step S1 is specifically as follows: n multi-omics data samples are denoted as in, is the nth sample, l is the category index of the omics data, l = 1, 2, ..., m; Normalize multi-omics data: in, is the normalized omics data of the lth category in the i-th sample, is the l-th type of omics data in the i-th sample, and Respectively The expectation and variance of The normalized omics data of different categories of the same sample are spliced ​​and fused to obtain the normalized sample, that is, the preprocessed sample: in, It represents the i-th sample after normalization or preprocessing.

5. A method for detecting cancer types based on multi-omics data clustering and re-labeling according to any one of claims 1-2, characterized in that: In step S2, clustering is performed using the K-means clustering algorithm, as shown below: S21, select K value: set the number of clusters to K, corresponding to K cancer categories; S22, initialization: samples with the same sample label are initialized into a cluster, the initialization cluster label is the same as the sample label, and the mean of the samples with the same sample label is calculated as the center of the initialization cluster; the mean is calculated using the sample features; S23, allocation: calculate the distance between each sample and each cluster center respectively, and assign each sample to the cluster corresponding to the nearest cluster center; The distance is calculated using sample features and cluster center features; S24, update: recalculate the cluster center of each cluster according to the current cluster assignment; S25, repeating steps S23 and S24 until the cluster center no longer changes or the specified maximum number of iterations is reached.

6. A method for detecting cancer types based on multi-omics data clustering and re-labeling according to any one of claims 1-2, characterized in that: In the category detection model, the preprocessed samples Sample feature X extracted using CNN network i ; Use the embedding layer to embed the preprocessed samples The sample feature pairs (X i ,X j ) is mapped into a feature space and the feature vector pair (E i ,E j ); Use Multi-Layer Perceptron (MLP) to predict cancer categories.

7. A readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method for detecting cancer categories based on multi-omics data clustering and re-labeling according to any one of claims 1 to 6 is implemented.

8. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a cancer category detection method based on multi-omics data clustering and re-labeling as described in any one of claims 1 to 6.

9. A computer program product, characterized in that It includes a computer program / instruction, which, when executed by a processor, implements a cancer category detection method based on multi-omics data clustering and re-labeling as described in any one of claims 1 to 6.

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