Genetic risk assessment system

Abstract

The invention relates to the technical field of genetics, in particular to a genetic risk assessment system which comprises a control terminal which is a main control terminal of the system and is used for sending out an execution command; the collection module is used for collecting genetic risk related parameters of the user; the analysis module is used for traversing the user genetic risk related parameters collected in the collection module and analyzing the similarity of the user genetic risk related parameters; the identification module is used for acquiring an analysis result of the similarity of the user genetic risk related parameters in the analysis module, and identifying the user genetic risk related parameters with the best user matching degree based on the analysis result of the similarity of the user genetic risk related parameters; according to the invention, by uploading, comparing and analyzing the relevant parameters of the genetic risk of the user, the early prevention condition of the genetic disease is effectively provided for the user, and the health management of the family of the user is facilitated, so that a better genetic disease prevention effect is brought to the next generation of user inoculation.

Description

Technical Field

[0001] The present invention relates to the technical field of genetics, and particularly relates to a genetic risk assessment system. Background Art

[0002] Heredity refers to the process by which biological individuals transmit their characteristic information to offspring through gene transmission. Genes are the basic units that determine the genetic characteristics of organisms. They are located on chromosomes and are recombined and transmitted during the reproductive process.

[0003] Genetic information includes various characteristics of organisms, such as appearance, traits, physiological functions, disease susceptibility, etc. Through heredity, offspring inherit a part of the gene combination from their parents and thus obtain characteristics similar to those of their parents.

[0004] Heredity not only plays an important role in the development and growth of individuals, but also has crucial significance for the evolution of species and adaptation to environmental changes. The laws and mechanisms of heredity are the core content of genetic research, which helps us understand the genetic diversity of organisms, gene mutations, the occurrence mechanisms of genetic diseases, etc.

[0005] However, there is currently no systematic method for assessing the risk of genetic disease onset and comprehensively analyzing genetic-related data, resulting in the genetic risk of human reproduction still relying on the experience accumulation assessment of medical staff, with relatively poor scientificity and accuracy. Summary of the Invention

[0006] In view of the above-mentioned drawbacks of the prior art, the present invention provides a genetic risk assessment system, which solves the technical problems put forward in the above background art.

[0007] To achieve the above object, the present invention is realized through the following technical solutions:

[0008] A genetic risk assessment system includes:

[0009] A control terminal, which is the main control end of the system and is used to issue execution commands;

[0010] A collection module, which is used to collect user genetic risk-related parameters;

[0011] An analysis module, which is used to traverse the user genetic risk-related parameters collected by the collection module and analyze the similarity of the user genetic risk-related parameters;

[0012] An identification module, which is used to obtain the analysis result of the similarity of the user genetic risk-related parameters in the analysis module and identify the user genetic risk-related parameters with the best user matching degree based on the analysis result of the similarity of the user genetic risk-related parameters;

[0013] A prediction module, configured to receive the user's genetic risk-related parameters recognized by the recognition module, and predict the disease genetic risk of the user on the system side based on the user's genetic risk-related parameters;

[0014] A feedback module, configured to receive the prediction result of the disease genetic risk of the user on the system side in the prediction module, and feedback it to the user on the system side.

[0015] Furthermore, a sub-module is provided under the collection module, including:

[0016] An upload unit, configured to upload the user's genetic risk-related parameters;

[0017] A storage unit, configured to receive the user's genetic risk-related parameters uploaded by the upload unit;

[0018] Among them, the user's genetic risk-related parameters uploaded by the upload unit are manually uploaded by the user on the system side. When the storage unit stores the user's genetic risk-related parameters, it distinguishes and stores the user's genetic risk-related parameters based on the attributes of the user's genetic risk-related parameters.

[0019] Furthermore, the user's genetic risk-related parameters uploaded by the upload unit include: gene data, family medical history, and lifestyle. Gene data, family medical history, and lifestyle are the attributes of the user's genetic risk-related parameters;

[0020] Among them, the upload unit uploads the user's genetic risk-related parameters in real time, and the storage unit synchronously receives and stores the newly uploaded user's genetic risk-related parameters by the upload unit. The collection target of the user's genetic risk-related parameters in the collection module is the user's parents and the direct relatives of the user's parents.

[0021] Furthermore, when the analysis module analyzes the similarity of the user's genetic risk-related parameters, the user's genetic risk-related parameters from the same user are used as a set of data, and any one of the Jaccard similarity coefficient and string similarity measurement algorithms is applied to perform the similarity analysis of the user's genetic risk-related parameters.

[0022] Furthermore, a sub-module is provided inside the analysis module, including:

[0023] A selection unit, configured to select the user's genetic risk-related parameters and feedback them to the analysis module;

[0024] Among them, when the selection unit selects the user's genetic risk-related parameters, the source of the user's genetic risk-related parameters is the storage unit. The user's genetic risk-related parameters selected by the selection unit are all data groups composed of the user's genetic risk-related parameters from the same user. Each time the selection unit runs, it selects two groups of the user's genetic risk-related parameters, and the two groups of the user's genetic risk-related parameters selected are not from the same user.

[0025] Furthermore, the user's genetic risk-related parameters with the best matching degree recognized by the recognition module are the group of the user's genetic risk-related parameters with the highest similarity analyzed by the analysis module.

[0026] Furthermore, a sub-module is set under the recognition module, including:

[0027] The retrieval unit is used to obtain the user's genetic risk-related parameters with the best matching degree recognized by the recognition module, take the obtained user's genetic risk-related parameters as the retrieval target, and retrieve the user's genetic risk-related parameters in the storage unit;

[0028] Among them, during the operation stage of the retrieval unit, when retrieving the user's genetic risk-related parameters in the storage unit, the retrieved user's genetic risk-related parameters are complete user's genetic risk-related parameters. After the retrieval unit retrieves the user's genetic risk-related parameters, it synchronously transmits them to the prediction module.

[0029] Furthermore, the collection target of the user's genetic risk-related parameters is the user's parents and the direct relatives of the user's parents. The children of the user's parents are the system-end users of this system.

[0030] Furthermore, the prediction result of the disease genetic risk of the system-end user in the prediction module is: among the family medical histories of the user's genetic risk-related parameters with the best matching degree recognized by the recognition module, the diseases that have been diagnosed by the user whose genetic risk-related parameters are the source.

[0031] Furthermore, the control terminal is electrically connected with a collection module through a medium. The collection module is electrically connected with an upload unit and a storage unit through a medium at a lower level. The collection module is electrically connected with an analysis module through a medium. The selection unit is electrically connected inside the analysis module. The selection unit is electrically connected to the storage unit through a medium. The analysis module is electrically connected with a recognition module through a medium. The recognition module is electrically connected with a retrieval unit through a medium. The retrieval unit is electrically connected to the storage unit through a medium. The recognition module is electrically connected with a prediction module and a feedback module through a medium. The prediction module is electrically connected with the retrieval unit through a medium.

[0032] Adopting the technical solution provided by the present invention, compared with the known public technology, it has the following beneficial effects:

[0033] The present invention provides a genetic risk assessment system. During operation, the system effectively provides early prevention conditions for genetic diseases for users through the upload, comparison, and analysis of the user's genetic risk-related parameters, is conducive to the health management of the user's family, and thereby is conducive to bringing better genetic disease prevention effects to the next generation born by the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0035] Figure 1 It is a schematic structural diagram of a genetic risk assessment system;

[0036] The reference numerals in the figure respectively represent: 1, control terminal; 2, collection module; 21, upload unit; 22, storage unit; 3, analysis module; 31, selection unit; 4, identification module; 41, retrieval unit; 5, prediction module; 6, feedback module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0038] The following further describes the present invention with reference to the embodiments.

[0039] Embodiment 1:

[0040] A genetic risk assessment system according to this embodiment, as Figure 1 shown, includes:

[0041] The control terminal 1, which is the main control end of the system and is used to issue execution commands;

[0042] The collection module 2, which is used to collect the user's genetic risk-related parameters;

[0043] The collection module 2 is provided with sub-modules at a lower level, including:

[0044] An upload unit 21 for uploading genetic risk-related parameters of a user;

[0045] A storage unit 22 for receiving the genetic risk-related parameters of the user uploaded by the upload unit 21;

[0046] Among them, the genetic risk-related parameters of the user uploaded by the upload unit 21 are manually uploaded by the user at the system end. When the storage unit 22 stores the genetic risk-related parameters of the user, it distinguishes and stores the genetic risk-related parameters of the user based on the attributes of the genetic risk-related parameters of the user;

[0047] An analysis module 3 for traversing the genetic risk-related parameters of the user collected by the collection module 2 and analyzing the similarity of the genetic risk-related parameters of the user;

[0048] There are sub-modules set inside the analysis module 3, including:

[0049] A selection unit 31 for selecting genetic risk-related parameters of the user to feedback to the analysis module 3;

[0050] Among them, when the selection unit 31 selects the genetic risk-related parameters of the user, the source of the genetic risk-related parameters of the user is the storage unit 22. The genetic risk-related parameters selected by the selection unit 31 are all data groups composed of the genetic risk-related parameters of the same user. Each time the selection unit 31 runs, it selects two groups of genetic risk-related parameters of the user, and the two groups of genetic risk-related parameters selected are not from the same user;

[0051] An identification module 4 for obtaining the analysis result of the similarity of the genetic risk-related parameters of the user in the analysis module 3 and identifying the genetic risk-related parameters of the user with the best match based on the analysis result of the similarity of the genetic risk-related parameters of the user;

[0052] There are sub-modules set under the identification module 4, including:

[0053] An extraction unit 41 for obtaining the genetic risk-related parameters of the user with the best match identified by the identification module 4, taking the obtained genetic risk-related parameters of the user as the extraction target, and extracting the genetic risk-related parameters of the user in the storage unit 22;

[0054] Among them, during the running stage of the extraction unit 41, when extracting the genetic risk-related parameters of the user in the storage unit 22, the extracted genetic risk-related parameters of the user are complete genetic risk-related parameters of the user. After the extraction unit 41 extracts the genetic risk-related parameters of the user, it synchronously transmits them to the prediction module 5;

[0055] A prediction module 5, configured to receive the user's genetic risk-related parameters recognized by the recognition module 4, and predict the disease genetic risk of the user on the system side based on the user's genetic risk-related parameters;

[0056] A feedback module 6, configured to receive the prediction result of the disease genetic risk of the user on the system side in the prediction module 5, and feedback it to the user on the system side;

[0057] The control terminal 1 is electrically connected to a collection module 2 through a medium. The lower level of the collection module 2 is electrically connected to an upload unit 21 and a storage unit 22 through a medium. The collection module 2 is electrically connected to an analysis module 3 through a medium. Inside the analysis module 3, a selection unit 31 is electrically connected through a medium. The selection unit 31 is electrically connected to the storage unit 22 through a medium. The analysis module 3 is electrically connected to a recognition module 4 through a medium. The recognition module 4 is electrically connected to a retrieval unit 41 through a medium. The retrieval unit 41 is electrically connected to the storage unit 22 through a medium. The recognition module 4 is electrically connected to a prediction module 5 and a feedback module 6 through a medium. The prediction module 5 is electrically connected to the retrieval unit 41 through a medium.

[0058] In this embodiment, the control terminal 1 controls the collection module 2 to collect the user's genetic risk-related parameters. The upload unit 21 synchronously uploads the user's genetic risk-related parameters. The storage unit 22 receives in real time the user's genetic risk-related parameters uploaded in the upload unit 21. The analysis module 3 further traverses the user's genetic risk-related parameters collected in the collection module 2 to analyze the similarity of the user's genetic risk-related parameters. The selection unit 31 synchronously selects the user's genetic risk-related parameters and feeds them back to the analysis module 3. The recognition module 4 runs later to obtain the analysis result of the similarity of the user's genetic risk-related parameters in the analysis module 3. Based on the analysis result of the similarity of the user's genetic risk-related parameters, the recognition module 4 identifies the user's genetic risk-related parameters with the best matching degree. The retrieval unit 41 synchronously obtains the user's genetic risk-related parameters with the best matching degree recognized by the recognition module 4, uses the obtained user's genetic risk-related parameters as the retrieval target, retrieves the user's genetic risk-related parameters in the storage unit 22, and then the prediction module 5 receives the user's genetic risk-related parameters recognized by the recognition module 4, and predicts the disease genetic risk of the user on the system side based on the user's genetic risk-related parameters. Finally, the feedback module 6 receives the prediction result of the disease genetic risk of the user on the system side in the prediction module 5, and feedbacks it to the user on the system side.

[0059] Embodiment 2:

[0060] At the specific implementation level, on the basis of Embodiment 1, this embodiment refers to Figure 1 to further specifically describe a genetic risk assessment system in Embodiment 1:

[0061] The user genetic risk-related parameters uploaded in the upload unit 21 include: genetic data, family medical history, and lifestyle. Genetic data, family medical history, and lifestyle are the attributes of the user genetic risk-related parameters.

[0062] Among them, the upload unit 21 uploads the user genetic risk-related parameters in real time, and the storage unit 22 synchronously receives and stores the newly uploaded user genetic risk-related parameters in the upload unit 21. The collection target of the user genetic risk-related parameters in the collection module 2 is the user's parents and the direct relatives of the user's parents.

[0063] As Figure 1 shown, when the analysis module 3 analyzes the similarity of the user genetic risk-related parameters, the user genetic risk-related parameters from the same user are used as a group of data, and any one of the Jaccard similarity coefficient and string similarity measurement algorithms is applied to perform the similarity analysis of the user genetic risk-related parameters.

[0064] As Figure 1 shown, the user genetic risk-related parameters with the best matching degree identified in the identification module 4 are the group of user genetic risk-related parameters with the highest similarity analyzed by the analysis module 3.

[0065] In this embodiment, through the above settings, further operational logic support is provided for the operation of the system in Embodiment 1, ensuring the stable operation of the system.

[0066] Embodiment 3:

[0067] At the specific implementation level, on the basis of Embodiment 1, this embodiment refers to Figure 1 to further specifically describe a genetic risk assessment system in Embodiment 1:

[0068] The collection target of the user genetic risk-related parameters is the user's parents and the direct relatives of the user's parents. The children of the user's parents are the system-end users of this system;

[0069] The prediction result of the disease genetic risk of the system-end user in the prediction module 5 is: in the family medical history of the user genetic risk-related parameters with the best matching degree identified in the identification module 4, the diseases that have been diagnosed in the source user of the user genetic risk-related parameters.

[0070] In summary, during the operation of the system in the above embodiments, through the upload, comparison, and analysis of the user genetic risk-related parameters, it effectively provides early prevention conditions for genetic diseases for users, is beneficial to the health management of the user's family, and thereby is beneficial to bringing better genetic disease prevention effects to the next generation born by the user.

[0071] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A genetic risk assessment system, characterized in that, Including: A control terminal (1), which is the main control end of the system and is used to issue execution commands; A collection module (2), which is used to collect genetic risk-related parameters of users; An analysis module (3), which is used to traverse the genetic risk-related parameters of users collected in the collection module (2) and analyze the similarity of the genetic risk-related parameters of users; An identification module (4), which is used to obtain the analysis result of the similarity of the genetic risk-related parameters of users in the analysis module (3), and based on the analysis result of the similarity of the genetic risk-related parameters of users, identify the genetic risk-related parameters of users with the best matching degree; A prediction module (5), which is used to receive the genetic risk-related parameters of users identified in the identification module (4) and predict the genetic disease risk of users on the system side based on the genetic risk-related parameters of users; A feedback module (6), which is used to receive the prediction result of the genetic disease risk of users on the system side in the prediction module (5) and feedback to the users on the system side.

2. The genetic risk assessment system according to claim 1, wherein A sub-module is set under the collection module (2), including: An upload unit (21), which is used to upload the genetic risk-related parameters of users; A storage unit (22), which is used to receive the genetic risk-related parameters of users uploaded in the upload unit (21); Among them, the genetic risk-related parameters of users uploaded in the upload unit (21) are manually uploaded by the users on the system side. When the storage unit (22) stores the genetic risk-related parameters of users, the genetic risk-related parameters of users are stored separately based on the attributes of the genetic risk-related parameters of users.

3. The genetic risk assessment system according to claim 2, wherein The genetic risk-related parameters of users uploaded in the upload unit (21) include: gene data, family medical history, and lifestyle. Gene data, family medical history, and lifestyle are the attributes of the genetic risk-related parameters of users; Among them, the upload unit (21) uploads the genetic risk-related parameters of users in real time, and the storage unit (22) synchronously receives and stores the newly uploaded genetic risk-related parameters of users in the upload unit (21). The collection target of the genetic risk-related parameters of users in the collection module (2) is the parents of the users and the direct relatives of the parents of the users.

4. A genetic risk assessment system according to claim 1, wherein When the analysis module (3) analyzes the similarity of the genetic risk-related parameters of users, each set of genetic risk-related parameters from the same user is used as a data group, and any one of the Jaccard similarity coefficient and string similarity measurement algorithms is used to perform the similarity analysis of the genetic risk-related parameters of users.

5. The genetic risk assessment system according to claim 1, wherein A sub-module is set inside the analysis module (3), including: A selection unit (31), which is used to select genetic risk-related parameters of users and feedback them to the analysis module (3); Among them, when the selection unit (31) selects the genetic risk-related parameters of users, the source of the genetic risk-related parameters of users is the storage unit (22). The genetic risk-related parameters selected by the selection unit (31) are all data groups composed of the genetic risk-related parameters from the same user. The selection unit (31) runs and selects two sets of genetic risk-related parameters each time, and the two sets of genetic risk-related parameters selected are not from the same user.

6. The genetic risk assessment system according to claim 1, wherein The user genetic risk-related parameter with the best matching degree identified in the identification module (4), that is, a set of user genetic risk-related parameters with the highest similarity analyzed by the analysis module (3).

7. The genetic risk assessment system according to claim 1, wherein Submodules are set under the identification module (4), including: The retrieval unit (41) is used to obtain the user genetic risk-related parameter with the best matching degree identified in the identification module (4), take the obtained user genetic risk-related parameter as the retrieval target, and retrieve the user genetic risk-related parameter in the storage unit (22). Among them, during the operation stage of the retrieval unit (41), when retrieving the user genetic risk-related parameter in the storage unit (22), the retrieved user genetic risk-related parameter is a complete user genetic risk-related parameter. After the retrieval unit (41) retrieves the user genetic risk-related parameter, it synchronously transmits it to the prediction module (5).

8. The genetic risk assessment system according to claim 1, wherein The collection target of the user's genetic risk-related parameters is the user's parents and the direct relatives of the user's parents. The children of the user's parents are the system-end users of this system.

9. The genetic risk assessment system according to claim 1, wherein The disease genetic risk prediction result of the system-end user in the prediction module (5) is: in the family medical history among the user genetic risk-related parameters with the best matching degree identified in the identification module (4), the user genetic risk-related parameter source user has been diagnosed with a disease.

10. The genetic risk assessment system according to claim 1, wherein The control terminal (1) is electrically connected to the collection module (2) through a medium. The collection module (2) is electrically connected to the upload unit (21) and the storage unit (22) through a medium at a lower level. The collection module (2) is electrically connected to the analysis module (3) through a medium. The analysis module (3) is internally electrically connected to the selection unit (31). The selection unit (31) is electrically connected to the storage unit (22) through a medium. The analysis module (3) is electrically connected to the identification module (4) through a medium. The identification module (4) is electrically connected to the retrieval unit (41) through a medium. The retrieval unit (41) is electrically connected to the storage unit (22) through a medium. The identification module (4) is electrically connected to the prediction module (5) and the feedback module (6) through a medium. The prediction module (5) is electrically connected to the retrieval unit (41) through a medium.

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