Cancer driving pathway identification system

Abstract

The invention relates to the technical field of cancer driving pathway recognition, in particular to a cancer driving pathway recognition system. The system comprises a memory, and program commands stored in the memory can be executed by a processor by the following steps: acquiring cancer gene mutation data; obtaining a first parameter of a gene pair by utilizing the cancer gene mutation data; constructing a gene mutation similarity model, and combining the gene mutation similarity model and the cancer gene mutation data to obtain gene mutation similarity parameters of the gene pair; establishing a cancer driving pathway recognition network through the first parameter, the gene mutation similarity parameter and the cancer gene mutation data; and identifying a cancer driving pathway based on the cancer driving pathway identification network. According to the method, various parameters are constructed according to the cancer gene mutation data, then the cancer driving pathway recognition network is constructed, finally the cancer driving pathway is recognized through the network, and the problem of recognizing the cancer driving pathway with high efficiency and low cost is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of cancer driver pathway identification, and in particular to a cancer driver pathway identification system. Background Art

[0002] Due to various factors such as changes in people's lifestyles, the aging of the population, and environmental pollution, the incidence and mortality of cancer are also rising, and it has become one of the most serious public health problems in the world.

[0003] The pathogenic mechanism of cancer is complex, and there are many factors that cause cancer. Existing studies have confirmed that the occurrence and development of cancer are closely related to genome mutations. Cancer is caused by the accumulation of abnormal gene mutations such as single nucleotide polymorphisms and copy number variations. Gene mutations may cause structural changes in proteins or differences in the expression of functional proteins, thereby causing an abnormal physiological environment and triggering cancer. This mutation that drives the occurrence of cancer is called a driver mutation, and the gene where the driver mutation site is located is called a driver gene. The corresponding mutations and genes that are not related to the occurrence of cancer are called passenger mutations and passenger genes. The accumulation of these passenger mutations / genes will not cause cancer.

[0004] Research has found that at different biological scales, driver genes usually aggregate to form driver pathways and jointly perform certain biological functions. These driver factors can disturb normal life activities. Therefore, identifying cancer driver pathways can help us gain a deeper understanding of the molecular mechanisms of cancer. By analyzing the gene mutations and interactions in these pathways, we can reveal the key links in the occurrence and development of cancer, and provide a theoretical basis for the prevention and treatment of cancer, which is specifically reflected in the following aspects: helping doctors to more accurately determine the type and stage of cancer in patients, so as to choose more accurate treatment methods and drugs, improve treatment effects and patient survival rates; providing new targets for drug development, and conducting drug research and development targeting key genes or molecules in the pathway can develop more effective anti-cancer drugs with fewer side effects to meet the needs of clinical treatment; it is conducive to a deeper understanding of the role and regulatory mechanism of biological molecules such as genes and proteins in the life process, and to contribute to the development of life sciences; by analyzing whether there are specific driver pathway mutations in the patient's body, it is possible to more accurately determine whether the patient has a certain type of cancer, providing strong support for early diagnosis and treatment.

[0005] However, it is difficult to accurately identify the driving factors associated with specific cancer categories from large-scale bio-omics data by relying solely on simple biological experiments or statistical analysis methods. Therefore, developing effective computational systems for large-scale cancer multi-omics genetic data to achieve accurate and efficient identification of cancer drivers and to find driving pathways that play an important role in the occurrence and development of cancer is a major challenge in current cancer informatics research. Summary of the invention

[0006] In view of the shortcomings of existing methods and the needs of practical applications, in order to solve the problem of identifying cancer driver pathways with high efficiency and low cost, the present invention provides a cancer driver pathway identification system, the cancer driver pathway identification system includes a memory, the memory is used to store a computer program, the computer program includes program instructions, when the program instructions are executed by a processor, the processor performs the following steps: Obtain cancer gene mutation data; use the cancer gene mutation data to obtain a first parameter of a gene pair; construct a gene mutation similarity model, and combine the gene mutation similarity model and the cancer gene mutation data to obtain gene mutation similarity parameters of the gene pair; establish a cancer driver pathway identification network through the first parameter, the gene mutation similarity parameter and the cancer gene mutation data; identify the cancer driver pathway based on the cancer driver pathway identification network.

[0007] The present invention utilizes cancer gene mutation data to construct a variety of parameters, and then constructs a cancer driver pathway identification network, and ultimately identifies cancer driver pathways through this network, solving the problem of efficient and low-cost identification of cancer driver pathways, providing a theoretical basis for deeper research and understanding of the pathogenesis of cancer, and helping researchers to strengthen their understanding of cancer. It has important theoretical and application value in many aspects, such as clinical diagnosis of cancer, development of targeted drugs, and precise and personalized treatment of cancer patients.

[0008] Optionally, the processor further performs the following steps: The cancer gene mutation data is scored; and the cancer gene mutation data is screened according to the gene scoring result. The present invention increases the proportion of driver genes by initially screening the gene mutation data score, which is beneficial to improving the calculation efficiency of the present invention.

[0009] Optionally, the gene scoring of the cancer gene mutation data satisfies the following formula: ,in, Indicates gene Rating, represents the number of cancer patient samples, Indicates gene The number of gene mutation types, Indicates gene In the The first The present invention uses a model to evaluate genes by the number of non-silent mutations in gene mutations, and the results are specifically quantified, making it easy to screen the cancer gene mutation data.

[0010] Optionally, the first parameter of a gene pair is obtained by using the cancer gene mutation data, satisfying the following formula: , in, Represents gene pair The first parameter, represents the minimum function, represents the counting function, Indicates gene The covering set of Indicates gene The first parameter constructed by the present invention is conducive to coordinating and controlling the density of the edges in the subsequent steps of establishing the network, and removing unnecessary edges further improves the reliability of the cancer driver pathway identification network.

[0011] Optionally, the constructed gene mutation similarity model satisfies the following formula: , in, Represents gene pair The gene mutation similarity parameter, Indicates gene The mutation frequency, Indicates gene The mutation frequency, represents the number of cancer patient samples, Indicates Genes from cancer patient samples To Gene The distance , Indicates Genes from cancer patient samples Mutation occurs, Indicates Genes from cancer patient samples Mutation occurs, Indicates gene The covering set of The present invention utilizes the gene mutation similarity between gene pairs to further remove unnecessary edges and improve the reliability of the cancer driver pathway identification network.

[0012] Optionally, establishing a cancer driver pathway identification network by using the first parameter, the gene mutation similarity parameter and the cancer gene mutation data comprises the following steps: According to the cancer gene mutation data, an initial cancer driver pathway identification network is established with genes as nodes; based on the first parameter and the gene mutation similarity parameter, the edges of the gene pairs in the initial cancer driver pathway identification network are determined; using the first parameter and the gene mutation similarity parameter, the weights of the edges are determined; combining the initial cancer driver pathway identification network, the edges and the weights, the cancer driver pathway identification network is established. The cancer driver pathway identification network constructed by the present invention uses multiple parameters to determine the edges and their weights, which is conducive to accurately identifying the driver pathway.

[0013] Optionally, the determining the edges of gene pairs in the cancer driver pathway identification initial network based on the first parameter and the gene mutation similarity parameter comprises the following steps: Setting a mutual exclusion threshold, a coverage threshold, a first parameter threshold, and a gene mutation similarity parameter threshold for the gene pair; determining the edges of the gene pairs in the cancer driver pathway identification initial network according to the edge construction rule; The edge construction rule satisfies: when the gene pair satisfies the mutual exclusion greater than the mutual exclusion threshold, the coverage greater than the coverage threshold, the first parameter greater than the first parameter threshold and the gene mutation similarity parameter greater than the gene mutation similarity parameter threshold, the gene pair has an undirected edge in the cancer driver pathway identification initial network; otherwise, the gene pair does not have an undirected edge in the cancer driver pathway identification initial network. The present invention further improves the reliability of the cancer driver pathway identification network by objectively determining the edges of the gene pairs in the network.

[0014] Optionally, the weight of the edge is determined by using the first parameter and the gene mutation similarity parameter to satisfy the following formula: , Represents gene pair represents the weight of the edge, Represents gene pair The first parameter, Represents gene pair The gene mutation similarity parameter, Represents gene pair The mutual exclusion degree of Represents gene pair The present invention uses multiple parameters to determine the weighting of edges, which can accurately reflect the relationship between gene pairs and further facilitate the identification of cancer driving pathways.

[0015] Optionally, identifying a cancer driver pathway based on the cancer driver pathway identification network comprises the following steps: Searching all maximal clusters in the cancer driver pathway identification network to obtain a maximal cluster set; calculating the cluster weight of the maximal cluster, using the cluster weight to obtain a maximal weight subcluster of the maximal cluster; combining the maximal cluster set and the maximal weight subcluster to obtain a cancer driver pathway set. The present invention transforms the problem of finding a driver pathway into the problem of finding a maximal cluster in a base network graph by constructing a cancer driver pathway identification network, which is conducive to improving the efficiency of identifying cancer driver pathways.

[0016] Optionally, the cancer driver pathway identification system further includes an input device, a processor and an output device, wherein the input device, the processor, the output device and the memory are interconnected, and the processor is configured to call and execute the program instructions. The cancer driver pathway identification system provided by the present invention has a compact structure and stable performance, which further enhances the overall applicability and practical application capability of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of program instructions for a cancer driver pathway identification system provided by an embodiment of the present invention; Figure 2 A framework diagram of a cancer driver pathway identification system provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a cancer driving pathway identification device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0019] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0020] See also Figure 1 In order to solve the problem of identifying cancer driver pathways with high efficiency and low cost, the present invention provides a cancer driver pathway identification system, the cancer driver pathway identification system includes a memory, the memory is used to store a computer program, the computer program includes program instructions, when the program instructions are executed by a processor, the processor performs the following steps: S1. Obtain cancer gene mutation data.

[0021] Gene is the basic material unit that carries genetic information and can guide the synthesis of protein through steps such as transcription and translation. In an embodiment, different cancer genomics data are obtained from a public cancer genomics database. Public cancer genomics databases include the Cancer Genome Atlas TCGA database, which is a large cancer genomics project jointly initiated by the National Cancer Institute and the National Human Genome Research Institute of the United States, covering more than 30 different types of cancer, and providing multi-omics data including DNA sequence, RNA sequence, DNA methylation, protein expression and clinical information; there are also some other commonly used cancer genomics databases, such as the International Cancer Genome Consortium ICGC database, COSMIC database, GDC database and NCBI database.

[0022] Furthermore, after obtaining the data, the data should be preprocessed to minimize data errors and achieve more accurate results. First, data cleaning is performed to supplement some missing values ​​and remove some existing noise; then data integration is performed to integrate data from different sources and characteristics based on multiple databases; finally, data redundancy is processed to make the data integrated, standardized and simplified.

[0023] In the embodiment, the acquired cancer gene mutation data can be expressed as The matrix ,Include Cancer patient samples, mutant genes, matrix The elements in satisfy: if there is a mutant gene, it is 1, and if there is no mutant gene, it is 0. It can be understood that the cancer patient samples can be patients with the same cancer or patients with different cancers. Therefore, this embodiment can ultimately identify cancer-driven single pathways and synergistic pathways.

[0024] Furthermore, in order to improve the calculation efficiency of the embodiment, the gene mutation data can also be scored to perform a preliminary screening to eliminate the mixed passenger genes, including the following steps: S11. Performing gene scoring on the cancer gene mutation data.

[0025] Specifically, the cancer gene mutation data is subjected to gene scoring to satisfy the following formula: ,in, Indicates gene Rating, represents the number of cancer patient samples, Indicates gene The number of gene mutation types, Indicates gene In the The first The number of non-silent mutations of each gene mutation type.

[0026] S12. Screening the cancer gene mutation data according to the gene scoring result.

[0027] Specifically, the larger the number of non-silent mutations in a gene, the larger the score result. Non-silent mutations can change the structure and function of gene products, and may further cause mutations that change biological traits. Therefore, the probability of a gene mutation becoming a passenger gene is reduced. The gene score result threshold can be reasonably set through experimental data to eliminate cancer gene mutation data with a gene score result less than the gene score result threshold. In the embodiment, the gene score result threshold is set to 0.1.

[0028] S2. Using the cancer gene mutation data, obtain a first parameter of a gene pair.

[0029] In the embodiment, the first parameter of the gene pair is obtained by using the cancer gene mutation data in step S2, which satisfies the following formula: , in, Represents gene pair The first parameter, represents the minimum function, represents the counting function, Indicates gene The covering set of Indicates gene The covering set.

[0030] It should be understood that the coverage set refers to the matrix of genes represented in cancer gene mutation data. The set of genes in , that is, the set of patients whose genes have mutated.

[0031] S3. Construct a gene mutation similarity model, and combine the gene mutation similarity model with the cancer gene mutation data to obtain gene mutation similarity parameters of gene pairs.

[0032] Specifically, the gene mutation similarity model constructed in step S3 satisfies the following formula: , in, Represents gene pair The gene mutation similarity parameter, Indicates gene The mutation frequency, Indicates gene The mutation frequency, represents the number of cancer patient samples, Indicates Genes from cancer patient samples To Gene The distance , Indicates Genes from cancer patient samples Mutation occurs, Indicates Genes from cancer patient samples Mutation occurs, Indicates gene The covering set of represents the counting function. The mutation frequency of a gene is the number of The proportion of patients with this gene mutation to the sample size.

[0033] S4. Establishing a cancer driver pathway identification network by using the first parameter, the gene mutation similarity parameter and the cancer gene mutation data.

[0034] In the embodiment, step S4, establishing a cancer driver pathway identification network by using the first parameter, the gene mutation similarity parameter and the cancer gene mutation data, comprises the following steps: S41. According to the cancer gene mutation data, an initial network for identifying cancer driver pathways is established with genes as nodes.

[0035] In an embodiment, the matrix Using genes as nodes, an initial network for identifying cancer driver pathways without boundaries was established.

[0036] S42. Based on the first parameter and the gene mutation similarity parameter, determine the edges of the gene pairs in the cancer driver pathway identification initial network.

[0037] Specifically, the mutual exclusivity threshold, coverage threshold, first parameter threshold and gene mutation similarity parameter threshold of the gene pair are set by experiments or expert discussion method; According to the edge construction rule, the edges of the gene pairs in the cancer driver pathway identification initial network are determined.

[0038] Furthermore, the edge construction rule satisfies: When the gene pair satisfies that the mutual exclusion is greater than the mutual exclusion threshold, the coverage is greater than the coverage threshold, the first parameter is greater than the first parameter threshold, and the gene mutation similarity parameter is greater than the gene mutation similarity parameter threshold, the gene pair has an undirected edge in the cancer driver pathway identification initial network; Otherwise, the gene pair has no undirected edge in the cancer driver pathway identification initial network.

[0039] It should be understood that the mutant gene network is constructed based on conditions such as high coverage and high mutual exclusivity satisfied between gene pairs, which can reduce the complexity of the algorithm, but the density of the number of network edges constructed under different conditions is also different. In the embodiment, the mutual exclusivity threshold is 0.85, the coverage threshold is 0.09, the first parameter threshold is 0.13, and the gene mutation similarity parameter threshold is 0.382.

[0040] S43: Determine the weight of the edge by using the first parameter and the gene mutation similarity parameter.

[0041] Specifically, in step S43, the weight of the edge is determined by using the first parameter and the gene mutation similarity parameter, satisfying the following formula: , Represents gene pair represents the weight of the edge, Represents gene pair The first parameter, Represents gene pair The gene mutation similarity parameter, Represents gene pair The mutual exclusion degree of Represents gene pair coverage.

[0042] Furthermore, the mutual exclusivity of the gene pair satisfies the following formula: , Represents gene pair The mutual exclusion degree of The coverage of gene pairs satisfies the following formula: , Represents gene pair coverage.

[0043] S44, combining the cancer driver pathway identification initial network, the edges and the weights to establish the cancer driver pathway identification network.

[0044] The edges are added to the cancer driver pathway identification initial network, and the edges are weighted using the weights to establish the cancer driver pathway identification network.

[0045] S5. Identify cancer driver pathways based on the cancer driver pathway identification network.

[0046] In an embodiment, the step S5 of identifying a cancer driver pathway based on the cancer driver pathway identification network comprises the following steps: S51, searching for all maximal clusters in the cancer driver pathway identification network to obtain a maximal cluster set.

[0047] Specifically, a variety of algorithms, including the Bron-Kerbosch algorithm, can be used to search for all maximal clusters in the cancer driver pathway identification network to obtain a maximal cluster set.

[0048] S52. Calculate the cluster weight of the maximum cluster, and use the cluster weight to obtain a maximum-weight sub-cluster of the maximum cluster.

[0049] The group weight of the maximum group is calculated to satisfy the following formula: ,in, Representation Group The group weight, Representation Group The node set mutual exclusion degree, Representation Group The node set coverage of Representation Group The sum of the edge weights of the node set.

[0050] Furthermore, for each maximal cluster, if the cluster weight of the subcluster obtained by deleting a node is higher than the weight before deleting the node, the subcluster is considered to be better; iteratively delete nodes until the weight no longer increases, and a maximal weight subcluster can be obtained.

[0051] S53. Combining the maximal cluster set and the maximal weighted sub-cluster set to obtain a cancer driving pathway set.

[0052] It should be understood that since the number of genes contained in the driver pathway is usually not less than 3, if the maximum weight subcluster meets the conditions: the number of nodes is greater than or equal to 3 and the coverage is greater than 0.3, then the maximum weight subcluster is determined to be a driver pathway, and all the maximum weight subclusters that meet the conditions are aggregated to form the cancer driver pathway set.

[0053] Therefore, this embodiment can identify a more complete cancer driving pathway in a shorter time, and does not need to pre-specify the number of genes in the driving pathway.

[0054] See also Figure 2 In an embodiment, the present invention provides a cancer driver pathway identification system, comprising: an input device, an output device, a processor, and a memory, wherein the input device, the output device, the processor, and the memory are interconnected, and the memory contains program instructions, and when the program instructions are executed by the processor, the following steps are performed: Obtain cancer gene mutation data; use the cancer gene mutation data to obtain a first parameter of a gene pair; construct a gene mutation similarity model, and combine the gene mutation similarity model and the cancer gene mutation data to obtain gene mutation similarity parameters of the gene pair; establish a cancer driver pathway identification network through the first parameter, the gene mutation similarity parameter and the cancer gene mutation data; identify the cancer driver pathway based on the cancer driver pathway identification network.

[0055] The cancer driving pathway identification system of the present invention has a compact structure and stable performance, which further enhances the overall applicability and practical application capability of the present invention.

[0056] In an embodiment, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. An input device may be used to obtain data information. An output device may be used to output the results obtained by storing program instructions contained in a computer program in a memory provided by the present invention. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory.

[0057] In yet another alternative embodiment, see Figure 3 , this embodiment also provides a cancer driving pathway identification device, such as Figure 3 As shown, including: The memory 10 is used to store computer programs; the processor 20 is used to execute the computer programs to implement the above-mentioned cancer driver pathway identification system program instruction steps. The memory 10, the processor 20, the communication interface 31 and the communication bus 32. The memory 10, the processor 20, and the communication interface 31 all communicate with each other through the communication bus 32.

[0058] In an embodiment, the memory 10 is used to store one or more program instructions. The memory 10 may store program instructions for implementing the following functions: Obtain cancer gene mutation data; use the cancer gene mutation data to obtain a first parameter of a gene pair; construct a gene mutation similarity model, and combine the gene mutation similarity model and the cancer gene mutation data to obtain gene mutation similarity parameters of the gene pair; establish a cancer driver pathway identification network through the first parameter, the gene mutation similarity parameter and the cancer gene mutation data; identify the cancer driver pathway based on the cancer driver pathway identification network.

[0059] In one possible implementation, the memory 10 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, and at least one application required for a function, etc.; the data storage area may store data created during use. In addition, the memory 10 may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0060] The processor 20 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic device, and the processor 20 may be a microprocessor or any conventional processor, etc. The processor 20 may call a program stored in the memory 10. The communication interface 31 may be an interface of a communication module, and is used to connect with other devices or systems.

[0061] Of course, it should be noted that Figure 3 The structure shown does not constitute a limitation on the cancer driving pathway identification device in this embodiment. In actual applications, the cancer driving pathway identification device may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0062] An embodiment also provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the program instruction steps of the cancer driver pathway identification system are implemented.

[0063] The storage medium may include: 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, and other media that can store program codes.

[0064] In summary, the present invention uses cancer gene mutation data to construct multiple parameters, and then constructs a cancer driver pathway identification network, and finally identifies cancer driver pathways through this network, solving the problem of high-efficiency and low-cost identification of cancer driver pathways, providing a theoretical basis for a deeper study and understanding of the pathogenesis of cancer, helping researchers to strengthen their understanding of cancer, and having important theoretical and application value in many aspects such as clinical diagnosis of cancer, targeted drug development, and precise and personalized treatment of cancer patients. Therefore, the present invention effectively overcomes the various shortcomings of the prior art and has a high industrial utilization value.

[0065] 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 it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope recorded in the present invention.

Claims

1. A cancer driver pathway identification system, characterized in that: The cancer driver pathway identification system includes a memory, the memory is used to store a computer program, the computer program includes program instructions, when the program instructions are executed by a processor, the processor performs the following steps: Obtain cancer gene mutation data; Using the cancer gene mutation data, obtaining a first parameter of a gene pair; Constructing a gene mutation similarity model, combining the gene mutation similarity model with the cancer gene mutation data, and obtaining gene mutation similarity parameters of gene pairs; Establishing a cancer driver pathway identification network through the first parameter, the gene mutation similarity parameter and the cancer gene mutation data; Based on the cancer driver pathway identification network, a cancer driver pathway is identified.

2. A cancer driver pathway identification system according to claim 1, characterized in that: The processor also performs the following steps: Performing gene scoring on the cancer gene mutation data; According to the gene scoring result, the cancer gene mutation data is screened.

3. A cancer driver pathway identification system according to claim 2, characterized in that: The gene scoring of the cancer gene mutation data satisfies the following formula: ,in, Indicates gene Rating, represents the number of cancer patient samples, Indicates gene The number of gene mutation types, Indicates gene In the The first The number of non-silent mutations of each gene mutation type.

4. A cancer driver pathway identification system according to claim 1, characterized in that: The first parameter of the gene pair is obtained by using the cancer gene mutation data, satisfying the following formula: ， in, Represents gene pair The first parameter, represents the minimum function, represents the counting function, Indicates gene The covering set of Indicates gene The covering set.

5. The cancer driver pathway identification system according to claim 1, characterized in that: The constructed gene mutation similarity model satisfies the following formula: ， in, Represents gene pair The gene mutation similarity parameter, Indicates gene The mutation frequency, Indicates gene The mutation frequency, represents the number of cancer patient samples, Indicates Genes from cancer patient samples To Gene The distance , Indicates Genes from cancer patient samples Mutation occurs, Indicates Genes from cancer patient samples Mutation occurs, Indicates gene The covering set of Represents a counting function.

6. A cancer driver pathway identification system according to claim 1, characterized in that: The method of establishing a cancer driver pathway identification network by using the first parameter, the gene mutation similarity parameter and the cancer gene mutation data comprises the following steps: According to the cancer gene mutation data, an initial network for identifying cancer driver pathways is established with genes as nodes; Based on the first parameter and the gene mutation similarity parameter, determining the edges of gene pairs in the cancer driver pathway identification initial network; Determining the weight of the edge using the first parameter and the gene mutation similarity parameter; The cancer driver pathway identification network is established by combining the cancer driver pathway identification initial network, the edges and the weights.

7. A cancer driver pathway identification system according to claim 6, characterized in that: The step of determining the edges of gene pairs in the cancer driver pathway identification initial network based on the first parameter and the gene mutation similarity parameter comprises the following steps: Set the mutual exclusivity threshold, coverage threshold, first parameter threshold and gene mutation similarity parameter threshold of gene pairs; Determining edges of gene pairs in the cancer driver pathway identification initial network according to edge construction rules; The edge construction rules satisfy: When the gene pair satisfies that the mutual exclusion is greater than the mutual exclusion threshold, the coverage is greater than the coverage threshold, the first parameter is greater than the first parameter threshold, and the gene mutation similarity parameter is greater than the gene mutation similarity parameter threshold, the gene pair has an undirected edge in the cancer driver pathway identification initial network; Otherwise, the gene pair has no undirected edge in the cancer driver pathway identification initial network.

8. The cancer driver pathway identification system according to claim 6, characterized in that: The weight of the edge is determined by using the first parameter and the gene mutation similarity parameter, satisfying the following formula: ， Represents gene pair represents the weight of the edge, Represents gene pair The first parameter, Represents gene pair The gene mutation similarity parameter, Represents gene pair The mutual exclusion degree of Represents gene pair coverage.

9. The cancer driver pathway identification system according to claim 1, characterized in that: The step of identifying a cancer driver pathway based on the cancer driver pathway identification network comprises the following steps: Searching for all maximal clusters in the cancer driver pathway identification network to obtain a maximal cluster set; Calculating the clique weight of the maximum clique, and using the clique weight to obtain a maximum-weight subcluster of the maximum clique; The maximal cluster set and the maximal weighted sub-clusters are combined to obtain a cancer driver pathway set.

10. The cancer driver pathway identification system according to any one of claims 1 to 9, characterized in that: It also includes an input device, a processor and an output device, wherein the input device, the processor, the output device and the memory are connected to each other, and the processor is configured to call and execute the program instructions.

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