CN116864001A - Animal model RNA expression quantitative analysis system and method based on AI - Google Patents
Animal model RNA expression quantitative analysis system and method based on AI Download PDFInfo
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Abstract
The invention provides an AI-based quantitative analysis system and method for RNA expression of an animal model, wherein the system comprises: the data acquisition module is used for acquiring RNA expression data of the animal model; the data processing module is used for screening the RNA expression data and extracting key characteristic data of the RNA expression data; the quantitative analysis module is used for carrying out quantitative analysis by utilizing an AI analysis model according to the key characteristic data to obtain a quantitative analysis result; and the analysis result display module is used for displaying the quantitative analysis result and the associated strategy information. The invention utilizes artificial intelligence technology to quantitatively analyze the RNA expression information of animal model, which can realize the effects of intelligence, high efficiency and high accuracy, reduce the workload of the drug biological analysis staff and improve the level of data analysis and animal experiment research.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to an animal model RNA expression quantitative analysis system and method based on AI.
Background
Ribonucleic acid (abbreviated as RNA) is a genetic information vector present in biological cells, as well as in part viruses, viroids; RNA is a single strand formed by transcription by taking one strand of DNA as a template and a base complementary pairing principle, and has the main functions of realizing the expression of genetic information on protein and being a bridge in the process of transferring the genetic information;
RNA is condensed from ribonucleotides via phosphodiester bonds to form a long-chain molecule; a ribonucleotide molecule is composed of phosphate, ribose and base, wherein the base of RNA has multiple classifications, and RNA has multiple classifications according to function distinction, for example mRNA is a protein synthesis template transcribed according to DNA sequence; tRNA is the identifier of the genetic code and the transporter of the amino acid on mRNA; rRNA is the part that constitutes the ribosome, which is the machinery for protein synthesis; there are also many kinds of small RNAs with different functions in the cell, including snRNA forming spliceosome, snRNA responsible for rRNA formation, miRNA and siRNA involved in RNAi effect, and ribozymes with the function of catalyzing biochemical reaction process, such as introns, RNase P, HDV, ribosomal RNA, etc.; the multiple classifications of RNA make its expression information diversified, the difficulty is greater in the research to the expression information;
the animal model refers to animal experimental objects and related materials with human disease simulation expression which are established in medical research; the research on animal models is related to the application science of experimental animals, and the biological characteristics and disease characteristics of various model animals are researched, so that the development process of animal diseases is researched.
AI refers to a new technical science that studies, develops theories, methods, techniques and application systems for simulating, extending and expanding human intelligence. For experimental study of animal models, analysis and study of RNA data of animals are needed to improve the requirements of the animal experimental study and the effect of the experimental study.
The traditional analysis method is adopted for the RNA data analysis of the animal model, the RNA sequence is compared with a reference genome through analysis tool software, and the analysis is carried out through sequence differences under a plurality of tissues or environments, but the methods have very high false positives, and the accuracy of the RNA data analysis can be influenced due to sequencing errors caused by a sequencing technology; because the carrier is various and complex in the existence of the RNA data, the content and the items of the RNA data are complicated, the RNA data are difficult to master and analyze comprehensively and thoroughly, the problems of low efficiency and low accuracy exist in the analysis process, the labor and time are required to be consumed, and the animal experiment research effect is influenced.
Patent application number CN201911378692.2 discloses an analysis method for identifying and quantifying expression of circular RNAs, which comprises a series of sequencing data filtering steps, sequencing data comparison steps, circular RNA junction identification steps, differential analysis steps, gene annotation steps and enrichment analysis steps. The analysis method can help to find new circRNA information, and further carry out identification and expression quantitative analysis on the circular RNA; however, the patent is analyzed by computer software, the method is complicated, analysis and calculation are needed based on designed software, and calculation errors and analysis errors are possible.
Therefore, there is a need to provide quantitative analysis systems and methods for AI-based RNA expression in animal models.
Disclosure of Invention
The invention provides an AI-based quantitative analysis system and a method for RNA expression of an animal model, which can realize the effects of intelligence, high efficiency and high accuracy by quantitatively analyzing the RNA expression information of the animal model by utilizing an artificial intelligence technology, reduce the workload of a drug biological analysis staff, and improve the level of data analysis and animal experiment research.
The invention provides an AI-based quantitative analysis system for RNA expression of an animal model, which comprises the following components:
the data acquisition module is used for acquiring RNA expression data of the animal model;
the data processing module is used for screening the RNA expression data and extracting key characteristic data of the RNA expression data;
the quantitative analysis module is used for carrying out quantitative analysis by utilizing an AI analysis model according to the key characteristic data to obtain a quantitative analysis result;
and the analysis result display module is used for displaying the quantitative analysis result and the associated strategy information.
Further, the data acquisition module comprises an animal model selection unit and a data extraction unit;
the animal model selecting unit is used for selecting a plurality of animal models according to the analysis requirement of the RNA expression data;
the data extraction unit is used for acquiring disease experimental data of the animal model and acquiring RNA expression data of the animal model according to pathological information in the disease experimental data.
Further, the data processing module comprises a data screening processing unit and a classifying processing unit;
the screening processing unit is used for carrying out normalization screening processing on the RNA expression data to obtain a first screening data set;
the classification processing unit is used for classifying the first screening data set based on a preset clustering model to obtain a plurality of classification results, and extracting key feature data of the obtained RNA expression data based on the classification results.
Further, the quantization analysis module comprises an AI analysis model construction unit and a quantization analysis unit;
the AI analysis model building unit is used for building an AI analysis model based on the deep neural network model and the unsupervised learning model;
and the quantitative analysis unit is used for carrying out AI analysis on the key characteristic data by utilizing the AI analysis model to obtain a quantitative analysis result.
Further, the AI analysis model construction unit comprises a model data acquisition subunit and an AI analysis model training verification subunit;
the model data acquisition subunit is used for acquiring first key feature data of the historical RNA expression data and generating a data training set and a data verification set based on the first key feature data;
and the AI analysis model training and verifying subunit is used for training and verifying the AI analysis model by utilizing the data training set and the data verifying set.
Further, the quantitative analysis unit comprises a prediction analysis subunit and an association analysis subunit;
the prediction analysis subunit is used for carrying out prediction analysis on the key characteristic data based on a first AI analysis submodel constructed by the deep neural network model to obtain the prediction probability of a prediction item of the RNA expression data;
the association analysis subunit is used for carrying out content item association degree analysis on the key characteristic data based on a second AI analysis submodel constructed by the unsupervised learning model to obtain an association distribution quantization chart of the content items of the RNA expression data; the degree of association is: the degree of association of the content item of the key characteristic data with the disease to be studied of the animal model.
Further, the analysis result display module comprises a quantitative analysis result display unit and a strategy information providing unit;
the quantitative analysis result display unit is used for displaying a quantitative analysis result based on a quantitative analysis program of the computer quantitative analysis platform or the mobile terminal;
and the strategy information providing unit is used for providing relevant strategy information by utilizing an association management platform of the computer quantitative analysis platform or an association management program of a quantitative analysis program of the mobile terminal.
Further, the system also comprises a policy information providing unit and an association management subunit, wherein the association management subunit comprises an association management library establishment subunit and a policy information display subunit;
the association management library building molecular unit is used for building a knowledge graph of the quantitative analysis result and a plurality of association research items based on a knowledge graph technology, and generating an association management library of the quantitative analysis result and the association research items according to the knowledge graph;
the policy information display molecular unit is used for setting policy information of the associated research item by utilizing a DOM tree structure based on the associated management library, and designing a tree display architecture and a paging display program for displaying the policy information of the associated research item.
Further, the system also comprises a model parameter optimization setting module, which is used for optimizing and adjusting the parameters of the AI model according to the historical quantitative analysis record of the AI model and setting a parameter library for setting the AI model; the model parameter optimization setting module comprises a parameter library construction unit and a parameter selection setting unit;
the parameter library construction unit is used for acquiring historical quantitative analysis records of the AI model and acquiring a plurality of historical parameters of the AI model and analysis errors of the corresponding AI model based on the historical quantitative analysis records; establishing a parameter correction calculation formula according to the analysis error and the historical parameters, and calculating to obtain the historical correction parameters; setting corresponding trimming amplitude values according to the historical correction parameters, generating a historical parameter sequence according to the trimming amplitude values and the historical correction parameters, and establishing a parameter library according to the historical parameter sequence;
the parameter selection setting unit is used for setting the parameter selection adjustment selection conditions of the AI model, setting the selection condition values, establishing a parameter selection calculation formula based on the selection condition values and the parameter library, selecting target parameters corresponding to the selection condition values in the parameter library by utilizing the parameter selection calculation formula, and using the target parameters for parameter adjustment of the AI model.
An AI-based quantitative analysis method for RNA expression in an animal model, comprising:
s1: obtaining RNA expression data of an animal model;
s2: screening the RNA expression data and extracting key characteristic data of the obtained RNA expression data;
s3: according to the key characteristic data, carrying out quantitative analysis by utilizing an AI analysis model to obtain a quantitative analysis result;
s4: and displaying the quantitative analysis result and the associated strategy information.
Compared with the prior art, the invention has the following advantages and beneficial effects: the invention utilizes artificial intelligence technology to quantitatively analyze the RNA expression information of animal model, which can realize the effects of intelligence, high efficiency and high accuracy, reduce the workload of the drug biological analysis staff and improve the level of data analysis and animal experiment research.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of an AI-based quantitative analysis system for RNA expression in animal models;
FIG. 2 is a schematic diagram of the data acquisition module of the AI-based animal model RNA expression quantitative analysis system;
FIG. 3 is a schematic diagram showing the steps of an AI-based quantitative analysis method for RNA expression in animal models.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The invention provides an AI-based quantitative analysis system for RNA expression of an animal model, as shown in figure 1, a data acquisition module is used for acquiring RNA expression data of the animal model;
the data processing module is used for screening the RNA expression data and extracting key characteristic data of the RNA expression data;
the quantitative analysis module is used for carrying out quantitative analysis by utilizing an AI analysis model according to the key characteristic data to obtain a quantitative analysis result;
and the analysis result display module is used for displaying the quantitative analysis result and the associated strategy information.
The working principle of the technical scheme is as follows: the data acquisition module is used for acquiring RNA expression data of the animal model;
the data processing module is used for screening the RNA expression data and extracting key characteristic data of the RNA expression data;
the quantitative analysis module is used for carrying out quantitative analysis by utilizing an AI analysis model according to the key characteristic data to obtain a quantitative analysis result;
and the analysis result display module is used for displaying the quantitative analysis result and the associated strategy information.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the quantitative analysis is carried out on the RNA expression information of the animal model by utilizing the artificial intelligence technology, so that the effects of intellectualization, high efficiency and high accuracy can be realized, the workload of a drug biological analysis staff can be lightened, and the level of data analysis and animal experiment research can be improved.
In one embodiment, as shown in fig. 2, the data acquisition module includes an animal model selection unit and a data extraction unit;
the animal model selecting unit is used for selecting a plurality of animal models according to the analysis requirement of the RNA expression data;
the data extraction unit is used for acquiring disease experimental data of the animal model and acquiring RNA expression data of the animal model according to pathological information in the disease experimental data.
The working principle of the technical scheme is as follows: the data acquisition module comprises an animal model selection unit and a data extraction unit;
the animal model selecting unit is used for selecting a plurality of animal models according to the analysis requirement of the RNA expression data;
the data extraction unit is used for acquiring disease experimental data of the animal model and acquiring RNA expression data of the animal model according to pathological information in the disease experimental data.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, through data acquisition, RNA expression data of the animal model can be acquired, and conditions are provided for subsequent data processing.
In one embodiment, the data processing module includes a data screening processing unit and a categorizing processing unit;
the screening processing unit is used for carrying out normalization screening processing on the RNA expression data to obtain a first screening data set;
the classification processing unit is used for classifying the first screening data set based on a preset clustering model to obtain a plurality of classification results, and extracting key feature data of the obtained RNA expression data based on the classification results.
The working principle of the technical scheme is as follows: the data processing module comprises a data screening processing unit and a classifying processing unit;
the screening processing unit is used for carrying out normalization screening processing on the RNA expression data to obtain a first screening data set;
the classification processing unit is used for classifying the first screening data set based on a preset clustering model to obtain a plurality of classification results, and extracting key feature data of the obtained RNA expression data based on the classification results.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the key characteristic data can be obtained through processing and extracting the RNA expression data, so that a basis can be provided for subsequent model analysis.
In one embodiment, the quantization analysis module includes an AI analysis model construction unit and a quantization analysis unit;
the AI analysis model building unit is used for building an AI analysis model based on the deep neural network model and the unsupervised learning model;
and the quantitative analysis unit is used for carrying out AI analysis on the key characteristic data by utilizing the AI analysis model to obtain a quantitative analysis result.
The working principle of the technical scheme is as follows: the quantitative analysis module comprises an AI analysis model construction unit and a quantitative analysis unit;
the AI analysis model building unit is used for building an AI analysis model based on the deep neural network model and the unsupervised learning model;
and the quantitative analysis unit is used for carrying out AI analysis on the key characteristic data by utilizing the AI analysis model to obtain a quantitative analysis result.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the analysis quality of the RNA expression data can be improved by constructing an AI analysis model and performing quantitative analysis.
In one embodiment, the AI analysis model construction unit includes a model data acquisition subunit and an AI analysis model training verification subunit;
the model data acquisition subunit is used for acquiring first key feature data of the historical RNA expression data and generating a data training set and a data verification set based on the first key feature data;
and the AI analysis model training and verifying subunit is used for training and verifying the AI analysis model by utilizing the data training set and the data verifying set.
The working principle of the technical scheme is as follows: the AI analysis model building unit comprises a model data acquisition subunit and an AI analysis model training and verifying subunit;
the model data acquisition subunit is used for acquiring first key feature data of the historical RNA expression data and generating a data training set and a data verification set based on the first key feature data;
and the AI analysis model training and verifying subunit is used for training and verifying the AI analysis model by utilizing the data training set and the data verifying set.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the construction quality of the AI analysis model can be improved by acquiring the model data and performing training verification of the model.
In one embodiment, the quantitative analysis unit comprises a predictive analysis subunit and an associative analysis subunit;
the prediction analysis subunit is used for carrying out prediction analysis on the key characteristic data based on a first AI analysis submodel constructed by the deep neural network model to obtain the prediction probability of a prediction item of the RNA expression data;
the association analysis subunit is used for carrying out content item association degree analysis on the key characteristic data based on a second AI analysis submodel constructed by the unsupervised learning model to obtain an association distribution quantization chart of the content items of the RNA expression data; the degree of association is: the degree of association of the content item of the key characteristic data with the disease to be studied of the animal model.
The working principle of the technical scheme is as follows: the quantitative analysis unit comprises a prediction analysis subunit and an association analysis subunit;
the prediction analysis subunit is used for carrying out prediction analysis on the key characteristic data based on a first AI analysis submodel constructed by the deep neural network model to obtain the prediction probability of a prediction item of the RNA expression data;
the association analysis subunit is used for carrying out content item association degree analysis on the key characteristic data based on a second AI analysis submodel constructed by the unsupervised learning model to obtain an association distribution quantization chart of the content items of the RNA expression data; the degree of association is: the degree of association of the content item of the key characteristic data with the disease to be studied of the animal model.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the quality and the efficiency of analysis can be improved by utilizing the AI molecular submodel to carry out predictive analysis and association analysis.
In one embodiment, the analysis result presentation module includes a quantitative analysis result presentation unit and a policy information providing unit;
the quantitative analysis result display unit is used for displaying a quantitative analysis result based on a quantitative analysis program of the computer quantitative analysis platform or the mobile terminal;
and the strategy information providing unit is used for providing relevant strategy information by utilizing an association management platform of the computer quantitative analysis platform or an association management program of a quantitative analysis program of the mobile terminal.
The working principle of the technical scheme is as follows: the analysis result display module comprises a quantitative analysis result display unit and a strategy information providing unit;
the quantitative analysis result display unit is used for displaying a quantitative analysis result based on a quantitative analysis program of the computer quantitative analysis platform or the mobile terminal;
and the strategy information providing unit is used for providing relevant strategy information by utilizing an association management platform of the computer quantitative analysis platform or an association management program of a quantitative analysis program of the mobile terminal.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the quantitative analysis result and the strategy information can be intuitively displayed by displaying the quantitative analysis result and the strategy information.
In one embodiment, the policy information providing unit further comprises an association management subunit, wherein the association management subunit comprises an association management library establishment subunit and a policy information presentation subunit;
the association management library building molecular unit is used for building a knowledge graph of the quantitative analysis result and a plurality of association research items based on a knowledge graph technology, and generating an association management library of the quantitative analysis result and the association research items according to the knowledge graph;
the policy information display molecular unit is used for setting policy information of the associated research item by utilizing a DOM tree structure based on the associated management library, and designing a tree display architecture and a paging display program for displaying the policy information of the associated research item.
The working principle of the technical scheme is as follows: the policy information providing unit also comprises an association management subunit, wherein the association management subunit comprises an association management library establishment molecular unit and a policy information display molecular unit;
the association management library building molecular unit is used for building a knowledge graph of the quantitative analysis result and a plurality of association research items based on a knowledge graph technology, and generating an association management library of the quantitative analysis result and the association research items according to the knowledge graph;
the policy information display molecular unit is used for setting policy information of the associated research item by utilizing a DOM tree structure based on the associated management library, and designing a tree display architecture and a paging display program for displaying the policy information of the associated research item.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the association management of the display result and the display of the strategy information are performed according to the knowledge graph technology, so that the efficiency of the association research of the RNA expression data is improved, and the research direction of the RNA expression data is expanded and the level of experimental research is improved.
In one embodiment, the system further comprises a model parameter optimization setting module, which is used for optimizing and adjusting parameters of the AI model according to the historical quantitative analysis record of the AI model and setting a parameter library for setting the AI model; the model parameter optimization setting module comprises a parameter library construction unit and a parameter selection setting unit;
the parameter library construction unit is used for acquiring historical quantitative analysis records of the AI model and acquiring a plurality of historical parameters of the AI model and analysis errors of the corresponding AI model based on the historical quantitative analysis records; establishing a parameter correction calculation formula according to the analysis error and the historical parameters, and calculating to obtain the historical correction parameters; setting corresponding trimming amplitude values according to the historical correction parameters, generating a historical parameter sequence according to the trimming amplitude values and the historical correction parameters, and establishing a parameter library according to the historical parameter sequence;
the parameter selection setting unit is used for setting the parameter selection adjustment selection conditions of the AI model, setting the selection condition values, establishing a parameter selection calculation formula based on the selection condition values and the parameter library, selecting target parameters corresponding to the selection condition values in the parameter library by utilizing the parameter selection calculation formula, and using the target parameters for parameter adjustment of the AI model.
The working principle of the technical scheme is as follows: the system also comprises a model parameter optimization setting module, a parameter database and a parameter analysis module, wherein the model parameter optimization setting module is used for optimizing and adjusting parameters of the AI model according to historical quantitative analysis records of the AI model and setting the parameter database for setting the AI model; the model parameter optimization setting module comprises a parameter library construction unit and a parameter selection setting unit;
the parameter library construction unit is used for acquiring historical quantitative analysis records of the AI model and acquiring a plurality of historical parameters of the AI model and analysis errors of the corresponding AI model based on the historical quantitative analysis records; establishing a parameter correction calculation formula according to the analysis error and the historical parameters, and calculating to obtain the historical correction parameters; setting corresponding trimming amplitude values according to the historical correction parameters, generating a historical parameter sequence according to the trimming amplitude values and the historical correction parameters, and establishing a parameter library according to the historical parameter sequence;
the parameter selection setting unit is used for setting the parameter selection adjustment selection conditions of the AI model, setting the selection condition values, establishing a parameter selection calculation formula based on the selection condition values and the parameter library, selecting target parameters corresponding to the selection condition values in the parameter library by utilizing the parameter selection calculation formula, and using the target parameters for parameter adjustment of the AI model.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, through adjusting the parameters of the AI model, the appropriate parameters can be selected for the AI model according to the parameter library, so that the application flexibility and analysis accuracy of the AI model are improved, and the analysis and research of different requirements can be conveniently adapted.
An AI-based quantitative analysis method for RNA expression in an animal model, as shown in fig. 3, comprises:
s1: obtaining RNA expression data of an animal model;
s2: screening the RNA expression data and extracting key characteristic data of the obtained RNA expression data;
s3: according to the key characteristic data, carrying out quantitative analysis by utilizing an AI analysis model to obtain a quantitative analysis result;
s4: and displaying the quantitative analysis result and the associated strategy information.
The working principle of the technical scheme is as follows: s1: obtaining RNA expression data of an animal model;
s2: screening the RNA expression data and extracting key characteristic data of the obtained RNA expression data;
s3: according to the key characteristic data, carrying out quantitative analysis by utilizing an AI analysis model to obtain a quantitative analysis result;
s4: and displaying the quantitative analysis result and the associated strategy information.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the quantitative analysis is carried out on the RNA expression information of the animal model by utilizing the artificial intelligence technology, so that the effects of intellectualization, high efficiency and high accuracy can be realized, the workload of a drug biological analysis staff can be lightened, and the level of data analysis and animal experiment research can be improved.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. An AI-based quantitative analysis system for RNA expression in an animal model, comprising:
the data acquisition module is used for acquiring RNA expression data of the animal model;
the data processing module is used for screening the RNA expression data and extracting key characteristic data of the RNA expression data;
the quantitative analysis module is used for carrying out quantitative analysis by utilizing an AI analysis model according to the key characteristic data to obtain a quantitative analysis result;
and the analysis result display module is used for displaying the quantitative analysis result and the associated strategy information.
2. The AI-based quantitative analysis system for RNA expression of an animal model of claim 1, wherein the data acquisition module comprises an animal model selection unit and a data extraction unit;
the animal model selecting unit is used for selecting a plurality of animal models according to the analysis requirement of the RNA expression data;
the data extraction unit is used for acquiring disease experimental data of the animal model and acquiring RNA expression data of the animal model according to pathological information in the disease experimental data.
3. The AI-based quantitative analysis system for RNA expression of an animal model of claim 1, wherein the data processing module comprises a data screening processing unit and a classification processing unit;
the screening processing unit is used for carrying out normalization screening processing on the RNA expression data to obtain a first screening data set;
the classification processing unit is used for classifying the first screening data set based on a preset clustering model to obtain a plurality of classification results, and extracting key feature data of the obtained RNA expression data based on the classification results.
4. The AI-based animal model RNA expression quantitative analysis system of claim 1, wherein the quantitative analysis module comprises an AI analysis model construction unit and a quantitative analysis unit;
the AI analysis model building unit is used for building an AI analysis model based on the deep neural network model and the unsupervised learning model;
and the quantitative analysis unit is used for carrying out AI analysis on the key characteristic data by utilizing the AI analysis model to obtain a quantitative analysis result.
5. The AI-based animal model RNA expression quantitative analysis system of claim 4, wherein the AI analysis model construction unit comprises a model data acquisition subunit and an AI analysis model training verification subunit;
the model data acquisition subunit is used for acquiring first key feature data of the historical RNA expression data and generating a data training set and a data verification set based on the first key feature data;
and the AI analysis model training and verifying subunit is used for training and verifying the AI analysis model by utilizing the data training set and the data verifying set.
6. The AI-based animal model RNA expression quantitative analysis system of claim 4, wherein the quantitative analysis unit comprises a predictive analysis subunit and an associative analysis subunit;
the prediction analysis subunit is used for carrying out prediction analysis on the key characteristic data based on a first AI analysis submodel constructed by the deep neural network model to obtain the prediction probability of a prediction item of the RNA expression data;
the association analysis subunit is used for carrying out content item association degree analysis on the key characteristic data based on a second AI analysis submodel constructed by the unsupervised learning model to obtain an association distribution quantization chart of the content items of the RNA expression data; the degree of association is: the degree of association of the content item of the key characteristic data with the disease to be studied of the animal model.
7. The AI-based animal model RNA expression quantitative analysis system of claim 1, wherein the analysis result presentation module comprises a quantitative analysis result presentation unit and a policy information providing unit;
the quantitative analysis result display unit is used for displaying a quantitative analysis result based on a quantitative analysis program of the computer quantitative analysis platform or the mobile terminal;
and the strategy information providing unit is used for providing relevant strategy information by utilizing an association management platform of the computer quantitative analysis platform or an association management program of a quantitative analysis program of the mobile terminal.
8. The AI-based animal model RNA expression quantitative analysis system of claim 7, wherein the policy information providing unit further comprises a correlation management subunit including a correlation management library creation molecular unit and a policy information presentation molecular unit;
the association management library building molecular unit is used for building a knowledge graph of the quantitative analysis result and a plurality of association research items based on a knowledge graph technology, and generating an association management library of the quantitative analysis result and the association research items according to the knowledge graph;
the policy information display molecular unit is used for setting policy information of the associated research item by utilizing a DOM tree structure based on the associated management library, and designing a tree display architecture and a paging display program for displaying the policy information of the associated research item.
9. The quantitative analysis system for the RNA expression of the animal model based on the AI according to claim 1, further comprising a model parameter optimization setting module, which is used for optimally adjusting the parameters of the AI according to the historical quantitative analysis record of the AI model and setting a parameter library for setting the AI model; the model parameter optimization setting module comprises a parameter library construction unit and a parameter selection setting unit;
the parameter library construction unit is used for acquiring historical quantitative analysis records of the AI model and acquiring a plurality of historical parameters of the AI model and analysis errors of the corresponding AI model based on the historical quantitative analysis records; establishing a parameter correction calculation formula according to the analysis error and the historical parameters, and calculating to obtain the historical correction parameters; setting corresponding trimming amplitude values according to the historical correction parameters, generating a historical parameter sequence according to the trimming amplitude values and the historical correction parameters, and establishing a parameter library according to the historical parameter sequence;
the parameter selection setting unit is used for setting the parameter selection adjustment selection conditions of the AI model, setting the selection condition values, establishing a parameter selection calculation formula based on the selection condition values and the parameter library, selecting target parameters corresponding to the selection condition values in the parameter library by utilizing the parameter selection calculation formula, and using the target parameters for parameter adjustment of the AI model.
10. The quantitative analysis method for the RNA expression of the animal model based on the AI is characterized by comprising the following steps:
s1: obtaining RNA expression data of an animal model;
s2: screening the RNA expression data and extracting key characteristic data of the obtained RNA expression data;
s3: according to the key characteristic data, carrying out quantitative analysis by utilizing an AI analysis model to obtain a quantitative analysis result;
s4: and displaying the quantitative analysis result and the associated strategy information.
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