CN113674797B - Proteome-based data detection system, method, apparatus and storage medium - Google Patents
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Abstract
The invention discloses a data detection system, a method, equipment and a storage medium based on a protein group, wherein the data detection system comprises the following components: a receiving module and an executing module; the receiving module is used for receiving information to be detected, and the information to be detected comprises proteome information; the execution module is used for inputting the information to be detected into a detection model to obtain a detection result. The invention can overcome the defect of deviation in prediction of diseases of organisms caused by whole genome sequencing, exon sequencing or mRNA detection of gene expression products in the prior art by detecting the proteins of the executors of the functions of the organisms, and can automatically acquire the detection result of the users by inputting the proteome information into a detection model, thereby avoiding the complexity and high duration of manual detection procedures and accurately reflecting the physiological function state of the users to be detected.
Description
Technical Field
The present invention relates to the field of automated analysis, and in particular, to a proteome-based data detection system, method, apparatus, and storage medium.
Background
In the prior art, various methods for detecting the data of an organism exist, in general, molecular characteristics in the organism can be detected by using a means of histology, in which whole genome sequencing or exon sequencing can analyze changes and abnormalities of a personal genome; in transcriptomics, changes in mRNA (gene expression product) in vivo are measured. The method can realize data detection to a certain extent, however, the histology technical means do not detect the executor protein of the body function, so that the executor protein can not accurately reflect the physiological state of the body.
Disclosure of Invention
The invention aims to overcome the defect that the detection result of the organism cannot reflect the accurate physiological state of the organism in the prior art, and provides a data detection system, a method, equipment and a storage medium based on a protein group.
The invention solves the technical problems by the following technical scheme:
The invention provides a data detection system based on proteome, comprising: a receiving module and an executing module;
The receiving module is used for receiving information to be detected, and the information to be detected comprises proteome information;
The execution module is used for inputting the information to be detected into a detection model to obtain a detection result.
Preferably, the data detection system further comprises a report generation module, which is used for automatically generating a detection report according to the detection result.
Preferably, the detection report comprises a mass spectrum of the proteomic analysis process.
Preferably, the detection result includes a prediction probability;
The execution module is further used for judging whether the prediction probability is greater than or equal to a preset probability, if yes, generating first prompt information, and if not, generating second prompt information;
And/or the number of the groups of groups,
The data detection system further includes a training module for inputting historical proteomic data into a machine learning model for training to obtain the detection model.
Preferably, the execution module includes: a detection flow acquisition unit and a prediction probability acquisition unit;
The detection flow acquisition unit is used for inputting the information to be detected into a detection model to acquire a corresponding detection flow;
the prediction probability obtaining unit is used for analyzing the information to be detected according to the detection flow so as to obtain the prediction probability.
Preferably, the prediction probability acquisition unit includes: the system comprises a flow analysis subunit, a task generation subunit, a task execution subunit and a probability generation subunit;
the flow analysis subunit is used for analyzing the detection flow;
the task generation subunit is used for generating execution tasks with different priorities according to the analyzed diagnosis flow;
The task execution subunit is used for sequentially executing the execution tasks according to the priorities of the execution tasks;
and the probability generation subunit is used for generating prediction probability after the execution of all the execution tasks is completed.
Preferably, the flow parsing subunit is configured to parse the diagnostic flow through Airflow (a workflow management framework);
And/or the number of the groups of groups,
The task execution subunit is used for receiving execution tasks with different priorities through Celery (a distributed task scheduling system) and distributing the execution tasks with different priorities into an execution process;
And/or the number of the groups of groups,
The data detection system also comprises a display module for displaying the execution state of the execution task;
And/or the number of the groups of groups,
The data detection system also comprises a log generation module which is used for generating an execution log of the execution task and storing the execution log into a database.
The invention also provides a data detection method based on the proteome, which comprises the following steps:
Receiving information to be detected, wherein the information to be detected comprises proteome information;
And inputting the information to be detected into a detection model to obtain a detection result.
Preferably, the data detection method further comprises the steps of: and automatically generating a detection report according to the detection result.
Preferably, the detection report comprises a mass spectrum of the proteomic analysis process.
Preferably, the detection result includes a prediction probability;
The step of inputting the information to be detected into a detection model to obtain a detection result comprises the following steps: judging whether the prediction probability is larger than or equal to a preset probability, if so, generating first prompt information, and if not, generating second prompt information;
And/or the number of the groups of groups,
The data detection method further comprises the steps of: historical proteomic data is input into a machine learning model for training to obtain the detection model.
Preferably, the step of inputting the information to be detected into a detection model to obtain a detection result further includes:
inputting the information to be detected into a detection model to obtain a corresponding detection flow;
and analyzing the information to be detected according to the detection flow to obtain the prediction probability.
Preferably, the step of analyzing the information to be detected according to the detection flow to obtain the prediction probability includes:
analyzing the detection flow;
generating execution tasks with different priorities according to the analyzed diagnosis flow;
Sequentially executing the execution tasks according to the priorities of the execution tasks;
and generating prediction probability after the execution of all the execution tasks is completed.
Preferably, the diagnostic procedure is resolved by Airflow;
And/or the number of the groups of groups,
Receiving execution tasks with different priorities through Celery and distributing the execution tasks with different priorities into an execution process;
And/or the number of the groups of groups,
The data detection method further comprises the steps of: displaying the execution state of the execution task;
And/or the number of the groups of groups,
The data detection method further comprises the steps of: and generating an execution log of the execution task, and storing the execution log into a database.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the data detection method as described above when executing the computer program.
The present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the data detection method as described above.
On the basis of conforming to the common knowledge in the field, the above preferred conditions can be arbitrarily combined to obtain the preferred examples of the invention.
The invention has the positive progress effects that: the invention can overcome the defect of deviation in prediction of diseases of organisms caused by whole genome sequencing, exon sequencing or mRNA detection of gene expression products in the prior art by detecting the proteins of the executors of the functions of the organisms, and can automatically acquire the detection result of users by inputting proteome information into a detection model, thereby avoiding the complexity and high duration of manual detection procedures and accurately reflecting the physiological function state of the users to be detected.
Drawings
FIG. 1 is a schematic block diagram of a proteome-based data detection system according to example 1 of the present invention.
FIG. 2 is a schematic block diagram of a proteome-based data detection system according to embodiment 2 of the present invention.
Fig. 3 is a schematic block diagram of an execution module according to embodiment 3 of the present invention.
Fig. 4 is a schematic block diagram of a prediction probability obtaining unit in embodiment 3 of the present invention.
Fig. 5 is a flowchart of a method for detecting proteome-based data according to embodiment 4 of the present invention.
FIG. 6 is a flow chart of a method for detecting proteome-based data according to embodiment 5 of the present invention.
Fig. 7 is a flowchart of an implementation of step 403 in embodiment 5 of the present invention.
Fig. 8 is a flowchart of an implementation of step 402 in embodiment 6 of the present invention.
Fig. 9 is a flowchart of an implementation of step 4022 in embodiment 6 of the present invention.
Fig. 10 is a schematic structural diagram of an electronic device in embodiment 7 of the present invention.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention.
Example 1
The present embodiment provides a data detection system based on proteome, as shown in fig. 1, the data detection system in the present embodiment includes: a receiving module 101 and an executing module 102.
The receiving module 101 is configured to receive information to be detected, and the executing module 102 is configured to input the information to be detected to the detection model to obtain a detection result.
Wherein the information to be detected comprises proteomic information of the user to be detected.
In this embodiment, by detecting a large amount of high-dimensional and structured proteomic data of a user to be detected, the defect that in the prior art, whole genome sequencing, exon sequencing or mRNA detection of a gene expression product deviates from disease prediction of an organism can be overcome, and by inputting proteomic information into a detection model, a detection result of the user can be automatically obtained, so that the complexity and high-duration of a manual detection procedure are avoided, and the physiological function state of the user to be detected can be accurately reflected.
Example 2
The present embodiment provides a data detection system based on proteome, which is a further improvement of embodiment 1, as shown in fig. 2, and the data detection system in this embodiment further includes a report generating module 103, configured to automatically generate a detection report according to the detection result.
The entries included in the detection report may be set according to actual requirements, for example, may include personal information of the user to be diagnosed, such as name, gender, age, etc., may include historical detection information of the user to be detected, may include sample information of a detection sample (such as a proteome sample) of the user to be detected, such as a sample sending date, a sample number, a sample source, a sample type, etc., may include detection information, such as a detection item, a detection number, a detection method, etc., may include detection result information, such as a mass spectrum result (a mass spectrum, a total protein identification number, etc.), a sample typing result (a cluster analysis map), etc., and may further include entries of a detection total conclusion, an explanation of the detection result, a detector, an auditor, a report date, etc.
Optionally, the detection report generating module 103 may further push the detection report to the user to be detected through a network push service.
Optionally, in order to further provide a detection result for the user to be detected, where the detection result includes a prediction probability, the execution module 102 is further configured to determine whether the prediction probability is greater than or equal to a preset probability, if so, generate the first prompt information, and if not, generate the second prompt information.
In specific practice, the first prompt information may be used to remind the user to be detected that the detection result is associated with a specific disease or used as a prediction reference for diagnosing a specific disease, and the second prompt information may be used to remind the user to be detected that the detection result is not associated with a specific disease or used as a prediction reference for not diagnosing a specific disease.
Optionally, in order to obtain a detection model with more reference value and more accurate prediction result, the proteome-based data detection system in this embodiment further includes a training module 104, configured to input historical proteomics data into the machine learning model for training to obtain the detection model.
Wherein the historical proteomic data may be derived from clinical proteomic data produced by a laboratory, it should be understood that the historical proteomic data may be updated continuously, such that the machine learning model may be further trained such that the detection model may also be updated continuously.
In this embodiment, a detection report may be automatically generated according to the detection result, so that the detection result is convenient for the user to view in a visual form.
In this embodiment, the execution module generates the prompt information according to the detection result, so as to further provide the user with more reference value information.
In this embodiment, the training module may train the machine learning model based on the historical proteomic data to obtain a detection model, where the detection model may implement operations of adding, updating, and deleting the detection flow according to different proteomic data and different usage methods, so as to automatically obtain a detection flow that is most matched with the proteomic data to be detected.
Example 3
The present embodiment provides a proteome-based data detection system, which is a further improvement of embodiment 1 or embodiment 2, as shown in fig. 3, and the execution module 102 specifically includes: the detection flow acquisition unit 1021 and the prediction probability acquisition unit 1022.
The detection process acquiring unit 1021 is configured to input the information to be detected into a detection model to acquire a corresponding detection process, and the prediction probability acquiring unit 1022 is configured to analyze the information to be detected according to the detection process to acquire a prediction probability.
Specifically, as shown in fig. 4, the prediction probability acquisition unit 1022 includes a flow analysis subunit 10221, a task generation subunit 10222, a task execution subunit 10223, and a probability generation subunit 10224.
The flow analysis subunit 10221 is configured to analyze the detection flow, the task generation subunit 10222 is configured to generate execution tasks with different priorities according to the analyzed diagnostic flow, the task execution subunit 10223 is configured to sequentially execute the execution tasks according to the priorities of the execution tasks, and the probability generation subunit 10224 is configured to generate a prediction probability after execution of all the execution tasks is completed.
The flow analysis subunit 10221 uses Airflow flow scheduling and monitoring services to complete analysis of the task flow dependency relationship, the task generation subunit 10222 generates task execution information according to the analyzed detection flow, issues the task to the message queues with different priority levels, and the task execution subunit 10223 uses Celery task execution management functions to receive the task in the message queue through Celery and distribute the task to the execution process.
Optionally, in order to facilitate querying of an execution state of each execution task, the data detection system in this embodiment further includes a display module and a log generation module, where the display module is configured to display the execution state of the execution task; the log generation module is used for generating an execution log of the execution task and storing the execution log into a database for Airflow to check.
The display module can be used for displaying the running state of each execution task in real time, recovering the structure and information of the submitted detection flow, and storing, deleting, sharing and analyzing the detection flow.
Example 4
The present embodiment provides a data detection method based on proteome, as shown in fig. 5, the data detection method includes:
Step 401, receiving information to be detected.
Step 402, inputting the information to be detected into a detection model to obtain a detection result.
Wherein the information to be detected comprises proteomic information of the user to be detected.
In this embodiment, by detecting a large amount of high-dimensional and structured proteomic data of a user to be detected, the defect that in the prior art, whole genome sequencing, exon sequencing or mRNA detection of a gene expression product deviates from disease prediction of an organism can be overcome, and by inputting proteomic information into a detection model, a detection result of the user can be automatically obtained, so that the complexity and high-duration of a manual detection procedure are avoided, and the physiological function state of the user to be detected can be accurately reflected.
Example 5
The present embodiment provides a data detection method based on proteome, which is a further improvement of embodiment 4, as shown in fig. 6, and step 402 further includes step 403, and automatically generating a detection report according to the detection result.
The entries included in the detection report may be set according to actual requirements, for example, may include personal information of the user to be diagnosed, such as name, gender, age, etc., may include historical detection information of the user to be detected, may include sample information of a detection sample (such as a proteome sample) of the user to be detected, such as a sample sending date, a sample number, a sample source, a sample type, etc., may include detection information, such as a detection item, a detection number, a detection method, etc., may include detection result information, such as a mass spectrum result (a mass spectrum, a total protein identification number, etc.), a sample typing result (a cluster analysis map), etc., and may further include entries of a detection total conclusion, an explanation of the detection result, a detector, an auditor, a report date, etc.
Optionally, in this embodiment, after the detection report is generated, the detection report may be further pushed to the user to be detected through a network push service.
Optionally, in order to further provide the detection result to the user to be detected, the detection result includes a prediction probability, as shown in fig. 7, step 403 may specifically include:
Step 501, judging whether the prediction probability is greater than or equal to a preset probability, if yes, executing step 502, and if not, executing step 503.
Step 502, generating first prompt information.
Step 503, generating a second prompt message.
In specific practice, the first prompt information may be used to remind the user to be detected that the detection result is associated with a specific disease or used as a prediction reference for diagnosing a specific disease, and the second prompt information may be used to remind the user to be detected that the detection result is not associated with a specific disease or used as a prediction reference for not diagnosing a specific disease.
Optionally, in order to obtain a detection model with a more reference value and a more accurate prediction result, the data detection method in this embodiment further includes the steps of: historical proteomic data is input into a machine learning model for training to obtain the detection model.
Wherein the historical proteomic data may be derived from clinical proteomic data produced by a laboratory, it should be understood that the historical proteomic data may be updated continuously, such that the machine learning model may be further trained such that the detection model may also be updated continuously.
In this embodiment, a detection report may be automatically generated according to the detection result, so that the detection result is convenient for the user to view in a visual form.
In this embodiment, the prompt information may be generated according to the detection result, so as to further provide the user with more reference value information.
In this embodiment, the machine learning model may be trained based on the historical proteomic data to obtain a detection model, and the detection model may implement operations of adding, updating, and deleting the detection flow according to different proteomic data and different usage methods, so as to automatically obtain a detection flow that is most matched with the proteomic data to be detected.
Example 6
The present embodiment provides a method for detecting data based on proteome, which is a further improvement of embodiment 4 or embodiment 5, as shown in fig. 8, step 402 specifically includes:
step 4021, inputting the information to be detected into a detection model to obtain a corresponding detection flow.
And step 4022, analyzing the information to be detected according to the detection flow to obtain a prediction probability.
Specifically, as shown in fig. 9, step 4022 may further include:
And 40221, analyzing the detection flow.
Step 40222, generating execution tasks with different priorities according to the parsed diagnosis flow
Step 40223, executing the executing tasks sequentially according to the priorities of the executing tasks.
Step 40224, generating a prediction probability after executing all the execution tasks.
In step 40221, the task flow dependency relationship is resolved by utilizing Airflow flow scheduling and monitoring service, in step 40222, task execution information is generated according to the resolved detection flow, and the tasks are issued to message queues with different priority levels, in step 40223, the tasks in the message queues are received by utilizing Celery task execution management function through Celery, and are distributed to execution processes.
Optionally, in order to facilitate querying the execution status of each execution task and the overall detection flow, the data detection method in this embodiment further includes the following steps:
Displaying the execution state of the execution task;
an execution log of the execution task is generated and stored in a database for Airflow inspection.
Restoring the structure and information of the submitted detection flow;
Save, delete, share and analyze the detection flow.
Example 7
The present embodiment provides an electronic device, which may be expressed in the form of a computing device (for example, may be a server device), including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor may implement any one of the data detection methods of embodiments 4 to 6 when executing the computer program.
Fig. 10 shows a schematic diagram of the hardware structure of the present embodiment, and as shown in fig. 10, the electronic device 9 specifically includes:
At least one processor 91, at least one memory 92, and a bus 93 for connecting the different system components (including the processor 91 and the memory 92), wherein:
The bus 93 includes a data bus, an address bus, and a control bus.
The memory 92 includes volatile memory such as Random Access Memory (RAM) 921 and/or cache memory 922, and may further include Read Only Memory (ROM) 923.
Memory 92 also includes a program/utility 925 having a set (at least one) of program modules 924, such program modules 924 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 91 executes various functional applications and data processing, such as any of the data detection methods of embodiments 4 to 6 of the present invention, by running a computer program stored in the memory 92.
The electronic device 9 may further communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication may occur through an input/output (I/O) interface 95. Also, the electronic device 9 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 via the bus 93. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the electronic device 9, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of an electronic device are mentioned, such a division is only exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Example 8
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any one of the data detection methods of embodiments 4 to 6.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps of implementing the data detection method of any of embodiments 4-6 when said program product is run on said terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.
Claims (12)
1. A proteome-based data detection system, the data detection system comprising: a receiving module and an executing module;
The receiving module is used for receiving information to be detected, and the information to be detected comprises proteome information;
the execution module is used for inputting the information to be detected into a detection model to obtain a detection result;
The execution module comprises: a detection flow acquisition unit and a prediction probability acquisition unit;
The detection flow acquisition unit is used for inputting the information to be detected into a detection model to acquire a corresponding detection flow;
the prediction probability acquisition unit is used for analyzing the information to be detected according to the detection flow so as to acquire prediction probability;
The data detection system further comprises a training module, a machine learning model and a data analysis module, wherein the training module is used for inputting historical proteomics data into the machine learning model for training to obtain the detection model;
the prediction probability acquisition unit includes: the system comprises a flow analysis subunit, a task generation subunit, a task execution subunit and a probability generation subunit;
the flow analysis subunit is used for analyzing the detection flow;
The task generation subunit is used for generating execution tasks with different priorities according to the analyzed detection flow;
The task execution subunit is used for sequentially executing the execution tasks according to the priorities of the execution tasks;
the probability generation subunit is used for generating prediction probability after the execution of all the execution tasks is completed;
The flow analysis subunit utilizes Airflow flow scheduling and monitoring services to complete analysis of task flow dependency relationship, the task generation subunit generates task execution information according to the analyzed detection flow, and issues the tasks to message queues with different priority levels, and the task execution subunit utilizes the task execution management function of Celery to receive the tasks in the message queues through Celery and distributes the tasks to execution processes.
2. The proteome-based data detection system of claim 1, further comprising a report generation module for automatically generating a detection report based on the detection result.
3. The proteome-based data detection system of claim 2, wherein the detection report includes a mass spectrum of the proteome analysis process.
4. The proteome-based data detection system of claim 1, wherein the detection results include a predictive probability;
the execution module is further configured to determine whether the prediction probability is greater than or equal to a preset probability, if yes, generate first prompt information, and if no, generate second prompt information.
5. The proteome-based data detection system of claim 4, wherein,
The data detection system also comprises a display module for displaying the execution state of the execution task;
And/or the number of the groups of groups,
The data detection system also comprises a log generation module which is used for generating an execution log of the execution task and storing the execution log into a database.
6. A proteome-based data detection method, the data detection method comprising:
Receiving information to be detected, wherein the information to be detected comprises proteome information;
inputting the information to be detected into a detection model to obtain a detection result;
the step of inputting the information to be detected into a detection model to obtain a detection result further comprises the following steps:
inputting the information to be detected into a detection model to obtain a corresponding detection flow;
analyzing the information to be detected according to the detection flow to obtain a prediction probability;
The data detection method further comprises the steps of: inputting historical proteomics data into a machine learning model for training to obtain the detection model;
the step of analyzing the information to be detected according to the detection flow to obtain the prediction probability comprises the following steps:
analyzing the detection flow;
generating execution tasks with different priorities according to the analyzed detection flow;
Sequentially executing the execution tasks according to the priorities of the execution tasks;
generating prediction probability after the execution of all the execution tasks is completed;
The method comprises the steps of completing analysis of task flow dependency relationship by utilizing Airflow flow scheduling and monitoring service, generating task execution information according to the analyzed detection flow, issuing tasks to message queues with different priority levels, receiving tasks in the message queues by utilizing a task execution management function of Celery through Celery, and distributing the tasks to execution processes.
7. The proteome-based data detection method of claim 6, wherein said data detection method further comprises the steps of: and automatically generating a detection report according to the detection result.
8. The proteome-based data detection method of claim 7, wherein the detection report comprises a mass spectrum of the proteome analysis process.
9. The proteome-based data detection method of claim 8, wherein the detection result includes a predictive probability;
the step of inputting the information to be detected into a detection model to obtain a detection result comprises the following steps: and judging whether the prediction probability is larger than or equal to a preset probability, if so, generating first prompt information, and if not, generating second prompt information.
10. The proteome-based data detection method of claim 9, wherein the data detection method further comprises the steps of: displaying the execution state of the execution task;
And/or the number of the groups of groups,
The data detection method further comprises the steps of: and generating an execution log of the execution task, and storing the execution log into a database.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the detection method according to any one of claims 6 to 10 when executing the computer program.
12. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor realizes the steps of the detection method according to any of claims 6 to 10.
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