CN113486231B - Chinese medicinal prescription and disease analysis method, analysis system, equipment and storage medium - Google Patents
Chinese medicinal prescription and disease analysis method, analysis system, equipment and storage medium Download PDFInfo
- Publication number
- CN113486231B CN113486231B CN202110694041.5A CN202110694041A CN113486231B CN 113486231 B CN113486231 B CN 113486231B CN 202110694041 A CN202110694041 A CN 202110694041A CN 113486231 B CN113486231 B CN 113486231B
- Authority
- CN
- China
- Prior art keywords
- information set
- target point
- disease
- preset
- intersection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 201000010099 disease Diseases 0.000 title claims abstract description 264
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 264
- 238000004458 analytical method Methods 0.000 title claims abstract description 137
- 239000003814 drug Substances 0.000 claims abstract description 126
- 150000001875 compounds Chemical class 0.000 claims description 100
- 238000004422 calculation algorithm Methods 0.000 claims description 69
- 230000009471 action Effects 0.000 claims description 57
- 239000011159 matrix material Substances 0.000 claims description 50
- 229940079593 drug Drugs 0.000 claims description 39
- 230000003993 interaction Effects 0.000 claims description 31
- 238000000034 method Methods 0.000 claims description 22
- 239000013598 vector Substances 0.000 claims description 22
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000010201 enrichment analysis Methods 0.000 claims description 13
- 238000009472 formulation Methods 0.000 claims description 9
- 239000000203 mixture Substances 0.000 claims description 9
- 238000001914 filtration Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 4
- 230000007246 mechanism Effects 0.000 abstract description 20
- 238000011160 research Methods 0.000 abstract description 10
- 239000003053 toxin Substances 0.000 description 9
- 231100000765 toxin Toxicity 0.000 description 9
- 208000025721 COVID-19 Diseases 0.000 description 8
- 108090000623 proteins and genes Proteins 0.000 description 6
- 238000003068 pathway analysis Methods 0.000 description 5
- 238000002474 experimental method Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 235000008216 herbs Nutrition 0.000 description 4
- 235000014347 soups Nutrition 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 241000411851 herbal medicine Species 0.000 description 3
- 210000004072 lung Anatomy 0.000 description 3
- 102000004169 proteins and genes Human genes 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000031018 biological processes and functions Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000004879 molecular function Effects 0.000 description 2
- 239000002547 new drug Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 241000711573 Coronaviridae Species 0.000 description 1
- 208000001528 Coronaviridae Infections Diseases 0.000 description 1
- 206010035664 Pneumonia Diseases 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000008236 biological pathway Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000003596 drug target Substances 0.000 description 1
- 229940088597 hormone Drugs 0.000 description 1
- 239000005556 hormone Substances 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000004060 metabolic process Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000037361 pathway Effects 0.000 description 1
- 230000000144 pharmacologic effect Effects 0.000 description 1
- 230000003389 potentiating effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000006916 protein interaction Effects 0.000 description 1
- 206010039073 rheumatoid arthritis Diseases 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 230000007723 transport mechanism Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/90—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to alternative medicines, e.g. homeopathy or oriental medicines
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H70/00—ICT specially adapted for the handling or processing of medical references
- G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Pharmacology & Pharmacy (AREA)
- Data Mining & Analysis (AREA)
- Alternative & Traditional Medicine (AREA)
- Chemical & Material Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Medicinal Chemistry (AREA)
- Toxicology (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The application discloses a traditional Chinese medicine prescription and a disease analysis method, an analysis system, equipment and a storage medium. The traditional Chinese medicine prescription, the disease analysis method, the analysis system, the equipment and the storage medium improve the accuracy and the reliability of a network pharmacology research mechanism.
Description
Technical Field
The application relates to the field of traditional Chinese medicine network pharmacology research, in particular to a traditional Chinese medicine prescription, a disease analysis method, an analysis system, equipment and a storage medium.
Background
The application of traditional Chinese medicine in the treatment of diseases has been in the history of thousands of years in China. Traditional Chinese medicine usually combines various herbal medicines to form a prescription, and can have good effect in treating diseases. The mechanism of treating diseases by the traditional Chinese medicine prescription mainly comprises various targets and approaches. At present, methods for researching the action mechanism of a traditional Chinese medicine prescription mainly comprise a clinical experiment method and a network pharmacological method. Clinical experiments are the best way to find the cure mechanism of the prescription, and provide the most powerful evidence for clarifying the cure mechanism of the prescription. However, not every place has experimental conditions, and if clinical experiments are performed on every traditional Chinese medicine in the initial stage of developing the medicine, the mechanism is analyzed, so that the step of developing new medicine is inevitably dragged, the efficiency of developing the medicine is reduced, and a method is needed to help reduce the space for searching the medicine in the initial stage, improve the medicine searching efficiency and give a prompt of acting new herb on the disease mechanism.
In 2007 Hopkins et al proposed the concept of "network pharmacology". They have found by analysis of drug-gene and protein interactions that most drugs act by indirectly modulating rather than directly acting on disease-associated proteins, and many potent drugs act by modulating multiple proteins rather than a single protein. Network pharmacology is expected to expand the space for searching drug targets. The development process of new drugs is accelerated, and the new drugs are widely applied by a plurality of researchers. Traditional Chinese medicine formulas based on network pharmacology act on disease mechanism research, and have some effective achievements, but have some disadvantages.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art. Therefore, the application provides a traditional Chinese medicine prescription and a disease analysis method, which can improve the accuracy and reliability of a network pharmacology research mechanism.
The application also provides a traditional Chinese medicine prescription and a disease analysis system.
The application also provides electronic equipment.
The present application also proposes a computer-readable storage medium.
According to the embodiment of the first aspect of the application, the traditional Chinese medicine prescription and the disease analysis method comprise the following steps:
obtaining compound information of herb according to herb information of traditional Chinese medicine prescription and a preset traditional Chinese medicine database, wherein the preset traditional Chinese medicine database comprises: matching information of the herb information and the compound information;
obtaining a drug action target point information set according to the compound information and a preset compound database, wherein the preset compound database comprises: matching information of the compound information and a drug action target information set;
obtaining a disease target point information set and a disease target point score information set according to disease information and a preset disease database, wherein the preset disease database comprises: matching information of the disease information and the disease target point information set and the disease target point score information set;
obtaining an intersection target point information set and an intersection disease target point score information set according to the drug action target point information set, the disease target point information set and the disease target point score information set;
and analyzing according to the compound information, the intersection target point information set, the intersection disease target point score information set and a preset target point interaction matrix by a preset algorithm to obtain an analysis result.
According to the traditional Chinese medicine prescription and the disease analysis method, the traditional Chinese medicine prescription has at least the following beneficial effects: by combining the compound information, the drug action target information set, the disease target information set and the preset target interaction matrix, a set of analysis mechanism for targets in a prescription-disease system is formed, an analysis result is obtained, and the accuracy and reliability of a network pharmacology research mechanism are improved.
According to some embodiments of the application, the disease target information set comprises: a filtered disease target information set and an unfiltered disease target information set;
the traditional Chinese medicine prescription and the disease analysis method also comprise the following steps:
obtaining a disease filtering action target point score information set according to the disease target point score information set and a preset target point score threshold value;
and obtaining the filtered disease target information set according to the filtered disease action target point score information set and the unfiltered disease target point information set.
According to some embodiments of the present application, the obtaining an intersection target information set and an intersection disease target score information set from the drug action target information set, the disease target information set, and the disease target score information set includes:
obtaining the intersection target information set according to the disease target information set and the drug action target information set;
and obtaining the intersection disease target point score information set according to the intersection target point information set and the disease target point score information set.
According to some embodiments of the application, the compound information includes: a herb weight vector and a herb compound matrix;
the analyzing according to the compound information, the intersection target point information set, the intersection disease target point score information set and the preset target point interaction matrix by a preset algorithm to obtain an analysis result comprises the following steps:
constructing a target compound matrix according to the intersection target information set and the compound information;
obtaining a herb target point score information set according to the herb weight vector, the herb compound matrix and the target point compound matrix;
and analyzing according to the herbal target point score information set, the intersection disease target point score information set, the intersection target point information set and a preset target point interaction matrix by a preset algorithm to obtain the analysis result.
According to some embodiments of the application, the preset algorithm includes: a PageRank algorithm and a preset analysis algorithm;
the analyzing according to the herb target point score information set, the intersection disease target point score information set and a preset target point interaction matrix by a preset algorithm to obtain the analysis result comprises the following steps:
iterating the herbal target point score information set and the intersection disease target point score information set by using a PageRank algorithm to obtain a herbal iteration target point score information set and an intersection iteration disease target point score information set;
obtaining an analysis target point score information set according to the herb iteration target point score information set, the intersection iteration disease target point score information set and a preset target point interaction matrix;
and analyzing the analysis target point score information set and the intersection target point information set by a preset analysis algorithm to obtain an analysis result.
According to some embodiments of the application, the preset analysis algorithm comprises: a preset enrichment analysis algorithm and a preset calculation algorithm;
the analyzing the analysis target point score information set by a preset analysis algorithm to obtain an analysis result comprises the following steps:
performing enrichment analysis on the intersection target information set by using a preset enrichment analysis algorithm to obtain an enrichment result;
obtaining a calculation target point score information set according to the enrichment result, the analysis target point score information set and a preset calculation algorithm;
and sorting the calculation target point score information set according to the score size to obtain the analysis result.
According to some embodiments of the application, the predetermined enrichment analysis algorithm is any one of the following: KEGG analysis algorithm, GO analysis algorithm.
According to a second aspect of the present application, a Chinese medicinal formulation and disease analysis system comprises:
the acquisition module is used for acquiring compound information of herb according to herb information of traditional Chinese medicine formulas and a preset traditional Chinese medicine database, and the preset traditional Chinese medicine database comprises: matching information of the herb information and the compound information;
the obtaining module is further configured to obtain a drug action target information set according to the compound information and a preset compound database, where the preset compound database includes: matching information of the compound information and a drug action target information set;
the acquisition module is further used for obtaining a disease target point information set and a disease target point score information set according to the disease information and a preset disease database, and the preset disease database comprises: matching information of the disease information and the disease target point information set and the disease target point score information set;
the processing module is used for obtaining an intersection target point information set and an intersection disease target point score information set according to the drug action target point information set, the disease target point information set and the disease target point score information set;
and the analysis module is used for analyzing according to the compound information, the intersection target point information set, the intersection disease target point score information set and a preset target point interaction matrix by a preset algorithm to obtain an analysis result.
The traditional Chinese medicine prescription and the disease analysis system according to the embodiment of the application have at least the following beneficial effects: by combining the compound information, the drug action target information set, the disease target information set and the preset target interaction matrix, a set of analysis mechanism for targets in a prescription-disease system is formed, an analysis result is obtained, and the accuracy and reliability of a network pharmacology research mechanism are improved.
An electronic device according to an embodiment of a third aspect of the present application includes: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the Chinese medicinal formulation and the disease analysis method according to any one of the embodiments of the first aspect when the computer program is executed.
According to a fourth aspect of the present application, there is provided a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the chinese medicine formulation and the disease analysis method according to any one of the first aspect.
Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
Drawings
The application is further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flowchart of a method for analyzing a disease and a traditional Chinese medicine prescription according to some embodiments of the present application;
FIG. 2 is a flowchart of a method for analyzing a disease and a traditional Chinese medicine prescription according to some embodiments of the present application;
FIG. 3 is a flowchart of a method for analyzing a disease and a traditional Chinese medicine prescription according to some embodiments of the present application;
FIG. 4 is a flowchart of a method for analyzing a disease and a traditional Chinese medicine prescription according to some embodiments of the present application;
FIG. 5 is a flowchart of a method for analyzing a disease and a traditional Chinese medicine prescription according to some embodiments of the present application;
FIG. 6 is a flowchart of a method for analyzing a disease and a traditional Chinese medicine prescription according to some embodiments of the present application;
fig. 7 is a block diagram of a traditional Chinese medicine prescription and a disease analysis system according to some embodiments of the present application.
Reference numerals: 100. an acquisition module; 200. a processing module; 300. and an analysis module.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
In the description of the present application, it should be understood that references to orientation descriptions, such as directions of up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the apparatus or element referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
In the description of the present application, the meaning of a number is one or more, the meaning of a number is two or more, and greater than, less than, exceeding, etc. are understood to exclude the present number, and the meaning of a number above, below, within, etc. are understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present application, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present application can be reasonably determined by a person skilled in the art in combination with the specific contents of the technical solution.
In the description of the present application, a description with reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Referring to fig. 1, in a first aspect, the present application provides a method for analyzing a disease and a traditional Chinese medicine formulation, including but not limited to step S100, step S200, step S300, step S400, and step S500.
Step S100: obtaining compound information of herbs according to herb information of traditional Chinese medicine formulas and a preset traditional Chinese medicine database, wherein the preset traditional Chinese medicine database comprises: matching information of herb information and compound information;
step S200: obtaining a drug action target information set according to the compound information and a preset compound database, wherein the preset compound database comprises: matching information of the compound information and the drug action target information set;
step S300: obtaining a disease target point information set and a disease target point score information set according to the disease information and a preset disease database, wherein the preset disease database comprises: matching information of the disease information and the disease target point information set and the disease target point score information set;
step S400: obtaining an intersection target point information set and an intersection disease target point score information set according to the drug action target point information set, the disease target point information set and the disease target point score information set;
step S500: analyzing according to the compound information, the intersection target point information set, the intersection disease target point score information set and the preset target point interaction matrix by a preset algorithm to obtain an analysis result.
The traditional Chinese medicine prescription generally contains a plurality of herbs, in order to obtain the components and action targets of the traditional Chinese medicine prescription, compound information corresponding to the herb information of the traditional Chinese medicine prescription is obtained from a preset traditional Chinese medicine database, and a medicine action target information set is obtained according to the compound information and the preset compound database, wherein the preset traditional Chinese medicine database comprises: matching information of herb information and compound information; the preset compound database comprises the following components: matching information of the compound information and the drug action target information set. For a disease, obtaining target point information of the disease from a preset disease database through specific disease information and collecting target point score information, namely obtaining a disease target point information set and a disease target point score information set, wherein the score value of disease action target point score information in the disease target point score information set represents the relation degree with the disease, the higher score means that the relation between the target point and the disease is larger, the lower score means that the relation between the target point corresponding to the score and the disease is not larger, and the preset disease database comprises: matching information of the disease information and the disease target point information set and the disease target point score information set. Taking intersection of the drug action target information set and the disease target information set to obtain an intersection target information set and an intersection disease target score information set corresponding to the intersection target information set. And analyzing the compound information, the intersection target point information set, the intersection disease target point score information set and the preset target point interaction matrix by a preset algorithm to obtain a final analysis result.
According to the traditional Chinese medicine prescription and the disease analysis method, a set of analysis mechanism for targets in a prescription-disease system is formed by combining the compound information, the medicine action target information set, the disease target information set and the preset target interaction matrix, so that an analysis result is obtained, and the accuracy and the reliability of a network pharmacology research mechanism are improved.
Specifically, the traditional Chinese medicine prescription generally contains concentrated herbal medicines, and in order to obtain the components and action targets of the traditional Chinese medicine prescription, compound information of the herbal medicines needs to be obtained from a preset traditional Chinese medicine database, for example, compound information can be obtained from a data center such as TCMSP (Traditional Chinese Medicine Database and Analysis Platform ). Since the compound names in different databases may be different, the compound names need to be unified, for example, the compound names may be obtained from a PubChem (small organic molecule bioactivity) database. Then, a drug action target information set is obtained according to a preset compound database and compound information, for example, target information corresponding to the compound is obtained in a TCMSP database, a PubCHem database and other databases, so as to obtain the drug action target information set, and it is understood that the drug action target information set is also target information of a traditional Chinese medicine prescription. Obtaining a disease target information set and a disease target score information set according to the disease information and a preset disease database, for example, obtaining a disease target from a DisGeNet database (a disease-related gene and mutation site database) and a GeneCards database, and collecting target score information to obtain the disease target information set and the disease target score information set. Taking intersection of the drug action target information set and the disease target information set to obtain an intersection target information set and an intersection disease target score information set corresponding to the intersection target information set. And analyzing the compound information, the intersection target point information set, the intersection disease target point score information set and the preset target point interaction matrix by a preset algorithm to obtain a final analysis result.
In the event that a specification is required, the preset target interaction matrix can be obtained from a target interaction database, for example, interaction data of targets are obtained from a STRING database, and then a symmetrical adjacent random matrix is constructed, so that the preset target interaction matrix can be obtained.
Referring to fig. 2, in some embodiments of the present application, a disease target information set includes: a filtered disease target information set and an unfiltered disease target information set; the Chinese medicinal preparation and the disease analysis method also comprise, but are not limited to, step S600 and step S700
Step S600: obtaining a disease filtering action target point score information set according to the disease target point score information set and a preset target point score threshold value;
step S700: and obtaining a filtered disease target information set according to the filtered disease action target score information set and the unfiltered disease target information set.
The score value of the disease action target point score information in the disease target point score information set represents the relation degree with the disease, the higher score means that the relation between the target point corresponding to the score value and the disease is larger, and the lower score means that the relation between the target point corresponding to the score value and the disease is not larger. The method comprises the steps of filtering out score information, which is lower than a preset target score threshold, in a disease target score information set by setting the preset target score threshold, leaving score information, which is higher than the preset target score threshold, so as to obtain a disease filtering action target score information set, finding corresponding target information from an unfiltered disease target information set according to the disease filtering action target score information set so as to obtain a filtered disease target information set, and acquiring an intersection of the obtained filtered disease target information set and a drug action target information set so as to perform subsequent analysis and obtain an analysis result.
By the arrangement, the targets with low score and small relation with the diseases are filtered, the targets with high score and large relation with the diseases are left, irrelevant or relevant targets can be removed, and the speed of subsequent calculation and analysis is improved.
It should be noted that, the preset target point score threshold is set manually in advance, and can also be obtained according to historical data analysis.
Referring to fig. 3, in some embodiments of the present application, step S400 includes, but is not limited to, step S410 and step S420.
Step S410: acquiring an intersection target information set according to the disease target information set and the drug action target information set;
step S420: and obtaining an intersection disease target point score information set according to the intersection target point information set and the disease target point score information set.
And acquiring an intersection of the disease target information set and the drug action target information set to obtain an intersection target information set, and finding score information corresponding to the targets in the intersection target information set from the disease target score information set to obtain an intersection disease target score information set. The action target point of the traditional Chinese medicine prescription is not identical with the target point of the disease, and a consistent target point is found before analysis, so that the accuracy of an analysis result can be improved, and the reliability and the accuracy of the traditional Chinese medicine prescription and the disease analysis method in the embodiment of the application are improved.
Referring to fig. 4, in some embodiments of the present application, the compound information includes: a herb weight vector and a herb compound matrix; step S500 includes, but is not limited to, step S510, step S520, and step S530.
Step S510: constructing a target compound matrix according to the intersection target information set and the compound information;
step S520: obtaining a herb target point score information set according to the herb weight vector, the herb compound matrix and the target point compound matrix;
step S530: and analyzing according to the herbal target point score information set, the intersection disease target point score information set, the intersection target point information set and the preset target point interaction matrix by a preset algorithm to obtain an analysis result.
Referring to fig. 5, in some embodiments of the present application, the preset algorithm includes: the PageRank algorithm and a preset analysis algorithm; step S530 includes, but is not limited to, step S531, step S532, and step S533.
Step S531: iterating the herbal target point score information set and the intersection disease target point score information set by using a PageRank algorithm to obtain a herbal iterative target point score information set and an intersection iterative disease target point score information set;
step S532: obtaining an analysis target point score information set according to the herbal iteration target point score information set, the intersection iteration disease target point score information set and a preset target point interaction matrix;
step S533: and analyzing the analysis target point score information set and the intersection target point information set by a preset analysis algorithm to obtain an analysis result.
Constructing a target point compound matrix according to the intersection target point information set and the compound information, obtaining a herbal target point score information set according to the herbal weight vector, the herbal compound matrix and the target point compound matrix, iterating the obtained herbal target point score information set and the intersection disease target point score information set through a PageRank algorithm to obtain a herbal iteration target point score information set and an intersection iteration disease target point score information set, obtaining an analysis target point score information set according to the herbal iteration target point score information set, the intersection iteration disease target point score information set and a preset target point interaction matrix, and finally analyzing the analysis target point score information set through a preset analysis algorithm to obtain an analysis result. By the arrangement, a set of analysis mechanism for targets in a prescription-disease system is formed by herb weight information (herb weight vector), a drug action target point information set, a disease target point information set and a preset target point interaction matrix of the traditional Chinese medicine prescription, an analysis result is obtained, and accuracy and reliability of a network pharmacology research mechanism are improved.
Specifically, first, the score vector of each target in the herb target point score information set is obtained, the score vector of the target is obtained through a formula (1), and the formula (1) is as follows:
V p ＝M 2 M 1 V h /max(M 2 M 1 V h ) (1)
in the formula (1), V p Score vector representing one target point in herb target point score information set, V h The weight vector of the herb is represented, namely the weight information of the herb; m is M 1 Representing a matrix of herbal compounds, matrix M 1 Each row represents a compound, each column represents a compound, and the matrix M 1 Wherein each value of 0 or 1 is used to indicate whether the herb contains such a compound, 0 indicates no content, and 1 indicates a content. M is M 2 Representing a matrix of target compounds, matrix M 2 Each row represents a target, each column represents a compound, matrix M 2 Is 0 or 1 to indicate whether the target is affected by such a compound, 0 indicating no effect and 1 indicating effect. And (3) obtaining the score vector of each target point through the formula (1), and obtaining the herb target point score information set.
Iterating the herb target point score information set and the intersection disease target point score information set according to a formula (2):
wherein V is 0 Representing target point score vectors in herb target point score information sets or intersection disease target point score information sets, V 1 Representing the target score vector after the first iteration, V 2 The target point score vector after the second iteration is represented, N represents the total number of target points, M is a preset target point interaction matrix, alpha is preset in a section (0, 1), and is generally 0.8, and can also refer to a value of 0.85 adopted by the PageRank algorithm of Google, and multiple attempts can be carried out according to actual conditions to find out reasonable values.
The herb target point score information set is iterated through a formula (2) to obtain an herb target point PageRank score vector, and the herb target point PageRank score vector comprises a plurality of iterated herb target points. And (3) iterating the intersection disease target point score information set through the formula (2) to obtain a disease target point PageRank score vector, wherein the disease target point PageRank score vector comprises a plurality of iterated disease target points.
The disease target PageRank score vector and the herb target PageRank score vector are averaged to obtain an analysis target score information set, and then the analysis target score information set and the intersection target information set are analyzed by a preset analysis algorithm to obtain an analysis result.
It should be noted that the PageRank algorithm is a web page ranking algorithm developed in 1998 by google, and is used for google search engines, and it uses the hyperlink in the web page and the importance weight of the web page to evaluate the credibility and importance of the web page on the internet. The PageRank algorithm utilizes hyperlink information between web pages to determine the most relevant documents, and similarly, the PageRank algorithm and interaction information between targets are adopted to determine the most relevant targets in a traditional Chinese medicine prescription-disease system.
Referring to fig. 6, in some embodiments of the present application, the preset analysis algorithm includes: a preset enrichment analysis algorithm and a preset calculation algorithm; step S533 includes, but is not limited to, step S5331, step S5332, and step S5333.
Step S5331: performing enrichment analysis on the intersection target point information set by using a preset enrichment analysis algorithm to obtain an enrichment result;
step S5332: analyzing the target point score information set and a preset calculation algorithm according to the enrichment result to obtain a calculation target point score information set;
step S5333: and sorting the calculation target point score information set according to the score size to obtain an analysis result.
In some embodiments of the present application, the predetermined enrichment analysis algorithm is any one of the following: KEGG analysis algorithm, GO analysis algorithm.
The method comprises the steps of carrying out enrichment analysis on targets in intersection target point information sets by a KEGG analysis algorithm or a GO analysis algorithm to obtain corresponding enrichment results, carrying out preset calculation algorithm on each enrichment result to obtain calculation target point scores, obtaining calculation target point score information sets by a plurality of calculation target point scores, and sorting the calculation target point scores in the calculation target point score information sets by score sizes, wherein the target point corresponding to the calculation target point score ranked first is the target point with the largest correlation.
The GO (Gene Ontology) database mainly contains three parts: cell fraction (cellular component, CC), molecular function (molecular function, MF), biological process (biological process, BP), and by GO analysis, it is possible to obtain which tissues, functions, processes, etc. of the human body the target is related to. KEGG (Kyoto Encyclopedia of Genes and Genomes, kyoto genome encyclopedia) database containing information about the pathway of action of humans, etc., for understanding advanced functions and utilities of biological systems such as cells, organisms and ecosystems from molecular level information, particularly large-scale molecular datasets generated by genomic sequencing and other high-throughput experimental techniques. By KEGG analysis we can get which biological pathways the target is mainly concentrated on. Typically, there are multiple results of GO or KEGG pathway analysis, so the PageRank score means are chosen for analysis.
Specifically, in this embodiment, the preset calculation algorithm is formula (3), where formula (3) is:
wherein p represents the value of each result in the GO analysis or KEGG pathway analysis, s i The PageRank score means of each target point of the negative electrode on each result in the GO analysis and KEGG channel analysis results are represented, the score r of each result can be obtained by carrying out score addition on the enrichment target point on each result and then utilizing the p value, and the score r is ranked, so that the final analysis result is obtained.
For example: taking the relation between the lung-heat clearing and toxin expelling soup and the COVID-19 as an example, the intersection targets of the 17 lung-heat clearing and toxin expelling soup and the COVID-19 are obtained, the 17 intersection targets are analyzed, the lower graph is the BP part analysis results of some GO, wherein with reference to the first result, count8 shows that 8 targets of the 17 targets are related to hormone metabolic process.
Table 1: analysis result of lung-heat clearing and toxin expelling decoction-COVID-19 GO
For example: referring to Table 2, the 17 intersection targets of the lung clearing and toxin expelling decoction-COVID-19 were subjected to KEGG analysis, and from Table 2, 5 targets were enriched on "Rheumatoid arthritis", suggesting some clues for the role of these targets.
Table 2: lung clearing toxin expelling soup-covd-19 KEGG pathway analysis result
For example: referring to Table 3, taking KEGG pathway analysis as an example, after applying equation (3), the relationship between the lung-heat clearing and toxin expelling soup and the COVID-19 is obtained, table 3 shows that 6 targets are enriched on the Coronavirus disease-COVID-19, the calculated score of the result is 1.703, and the strong relationship between the lung-heat clearing and toxin expelling soup and the COVID-19 is shown as the most of all the results, which is consistent with some relevant clinical reports: according to the report of the national administration officials, the clinical observation of the 4 provinces of trial points shows that the total effective rate of the lung-heat clearing and toxin expelling decoction for treating the novel coronavirus infection of patients with pneumonia can reach more than 90 percent.
TABLE 3 score ranking of lung clearing and toxin expelling decoction-COVID-19 KEGG pathway analysis results
Therefore, the traditional Chinese medicine prescription and the disease analysis method of the embodiment of the application improve the accuracy and reliability of a network pharmacology research mechanism.
Referring to fig. 7, in a second aspect, some embodiments of the present application further provide a traditional Chinese medicine prescription and a disease analysis system, including: an acquisition module 100, a processing module 200 and an analysis module 300.
The obtaining module 100 is configured to obtain herb compound information according to herb information in a traditional Chinese medicine prescription and a preset traditional Chinese medicine database, where the preset traditional Chinese medicine database includes: matching information of herb information and compound information; the obtaining module 100 is further configured to obtain a drug action target information set according to the compound information and a preset compound database, where the preset compound database includes: matching information of the compound information and the drug action target information set; the obtaining module 100 is further configured to obtain a disease target information set and a disease target score information set according to disease information and a preset disease database, where the preset disease database includes: matching information of the disease information and the disease target point information set and the disease target point score information set.
The processing module 200 is configured to obtain an intersection target information set and an intersection disease target score information set according to the drug action target information set, the disease target information set and the disease target score information set.
The analysis module 300 is configured to analyze the compound information, the intersection target point information set, the intersection disease target point score information set, and the preset target interaction matrix according to a preset algorithm to obtain an analysis result.
The traditional Chinese medicine prescription generally contains a plurality of herbs, in order to obtain the components and action targets of the traditional Chinese medicine prescription, compound information of the traditional Chinese medicine herbs is obtained from a preset traditional Chinese medicine database, and a medicine action target information set is obtained according to the compound information and the preset compound database, wherein the preset traditional Chinese medicine database comprises: matching information of herb information and compound information; the preset compound database comprises the following components: matching information of the compound information and the drug action target information set. For a disease, obtaining target point information of the disease from a preset disease database through specific disease information and collecting target point score information, namely obtaining a disease target point information set and a disease target point score information set, wherein a score value of first disease action target point score information in the disease target point score information set represents the relation degree with the disease, a higher score means that the relation between the target point and the disease is larger, a lower score means that the relation between the target point corresponding to the score value and the disease is not larger, and the preset disease database comprises: matching information of the disease information and the disease target point information set and the disease target point score information set. Taking intersection of the drug action target information set and the disease target information set to obtain an intersection target information set and an intersection disease target score information set corresponding to the intersection target information set. And analyzing the compound information, the intersection target point information set, the intersection disease target point score information set and the preset target point interaction matrix by a preset algorithm to obtain a final analysis result.
According to the traditional Chinese medicine prescription and the disease analysis system, a set of analysis mechanism for targets in the prescription-disease system is formed by combining the compound information, the medicine action target information set, the disease target information set and the preset target interaction matrix, so that an analysis result is obtained, and the accuracy and the reliability of a network pharmacology research mechanism are improved.
The subsequent analysis methods of the traditional Chinese medicine prescription and the disease analysis system in the embodiment of the present application are similar to the aforementioned traditional Chinese medicine prescription and disease analysis method, and are not described here again.
In a third aspect, an embodiment of the present application further provides an electronic device.
In some embodiments, an electronic device includes: at least one processor, and a memory communicatively coupled to the at least one processor; the memory stores instructions that are executed by the at least one processor, so that the at least one processor can implement any one of the Chinese medicinal formulas and the disease analysis method according to the embodiments of the present application when executing the instructions.
The processor and the memory may be connected by a bus or other means.
The memory is used as a non-transitory computer readable storage medium for storing non-transitory software programs and non-transitory computer executable programs, such as the traditional Chinese medicine formulas and disease analysis methods described in the embodiments of the present application. The processor executes the non-transient software program and instructions stored in the memory, thereby realizing the traditional Chinese medicine prescription and the disease analysis method.
The memory may include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area can store and execute the traditional Chinese medicine prescription and the disease analysis method. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The non-transitory software programs and instructions required to implement the above-described chinese medicine formulation and disease analysis method are stored in the memory, which when executed by the one or more processors, perform the chinese medicine formulation and disease analysis method as set forth in the embodiments of the first aspect described above.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium.
In some embodiments, a computer-readable storage medium stores computer-executable instructions for performing the chinese medicinal formulation and the disease analysis method mentioned in the embodiments of the first aspect.
In some embodiments, the storage medium stores computer-executable instructions that are executed by one or more control processors, e.g., by one of the processors in the electronic device, to cause the one or more processors to perform the chinese medicine formulation and the disease analysis method.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
The embodiments of the present application have been described in detail above with reference to the accompanying drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present application. Furthermore, embodiments of the present application and features of the embodiments may be combined with each other without conflict.
Claims (10)
1. The Chinese medicinal prescription and the disease analysis method are characterized by comprising the following steps:
obtaining compound information of herb according to herb information of traditional Chinese medicine prescription and a preset traditional Chinese medicine database, wherein the preset traditional Chinese medicine database comprises: matching information of the herb information and the compound information;
obtaining a drug action target point information set according to the compound information and a preset compound database, wherein the preset compound database comprises: matching information of the compound information and a drug action target information set;
obtaining a disease target point information set and a disease target point score information set according to disease information and a preset disease database, wherein the preset disease database comprises: matching information of the disease information and the disease target point information set and the disease target point score information set;
obtaining an intersection target point information set and an intersection disease target point score information set according to the drug action target point information set, the disease target point information set and the disease target point score information set;
and analyzing according to the compound information, the intersection target point information set, the intersection disease target point score information set and a preset target point interaction matrix by a preset algorithm to obtain an analysis result.
2. The traditional Chinese medicine prescription and disease analysis method according to claim 1, wherein the disease target information set comprises: a filtered disease target information set and an unfiltered disease target information set;
the traditional Chinese medicine prescription and the disease analysis method also comprise the following steps:
obtaining a disease filtering action target point score information set according to the disease target point score information set and a preset target point score threshold value;
and obtaining the filtered disease target information set according to the filtered disease action target point score information set and the unfiltered disease target point information set.
3. The method according to claim 1 or 2, wherein the obtaining an intersection target information set and an intersection disease target score information set from the drug action target information set, the disease target information set, and the disease target score information set comprises:
obtaining the intersection target information set according to the disease target information set and the drug action target information set;
and obtaining the intersection disease target point score information set according to the intersection target point information set and the disease target point score information set.
4. The method of claim 1, wherein the compound information comprises: a herb weight vector and a herb compound matrix;
the analyzing according to the compound information, the intersection target point information set, the intersection disease target point score information set and the preset target point interaction matrix by a preset algorithm to obtain an analysis result comprises the following steps:
constructing a target compound matrix according to the intersection target information set and the compound information;
obtaining a herb target point score information set according to the herb weight vector, the herb compound matrix and the target point compound matrix;
and analyzing according to the herbal target point score information set, the intersection disease target point score information set, the intersection target point information set and the preset target point interaction matrix by a preset algorithm to obtain the analysis result.
5. The method for analyzing a disease according to claim 4, wherein the predetermined algorithm comprises: a PageRank algorithm and a preset analysis algorithm;
the analyzing according to the herb target point score information set, the intersection disease target point score information set, the intersection target point information set and the preset target point interaction matrix by a preset algorithm to obtain the analysis result comprises the following steps:
iterating the herbal target point score information set and the intersection disease target point score information set by using a PageRank algorithm to obtain a herbal iteration target point score information set and an intersection iteration disease target point score information set;
obtaining an analysis target point score information set according to the herb iteration target point score information set, the intersection iteration disease target point score information set and the preset target point interaction matrix;
and analyzing the analysis target point score information set and the intersection target point information set by a preset analysis algorithm to obtain the analysis result.
6. The method according to claim 5, wherein the predetermined analysis algorithm comprises: a preset enrichment analysis algorithm and a preset calculation algorithm;
analyzing the analysis target point score information set and the intersection target point information set by a preset analysis algorithm to obtain the analysis result, wherein the analysis result comprises the following steps:
performing enrichment analysis on the intersection target information set by using a preset enrichment analysis algorithm to obtain an enrichment result;
obtaining a calculation target point score information set according to the enrichment result, the analysis target point score information set and a preset calculation algorithm;
and sorting the calculation target point score information set according to the score size to obtain the analysis result.
7. The method according to claim 6, wherein the predetermined enrichment analysis algorithm is any one of the following: KEGG analysis algorithm, GO analysis algorithm.
8. The Chinese medicinal prescription and the disease analysis system are characterized by comprising the following components:
the acquisition module is used for acquiring compound information of herb according to herb information of traditional Chinese medicine formulas and a preset traditional Chinese medicine database, and the preset traditional Chinese medicine database comprises: matching information of the herb information and the compound information;
the obtaining module is further configured to obtain a drug action target information set according to the compound information and a preset compound database, where the preset compound database includes: matching information of the compound information and a drug action target information set;
the acquisition module is further used for obtaining a disease target point information set and a disease target point score information set according to the disease information and a preset disease database, and the preset disease database comprises: matching information of the disease information and the disease target point information set and the disease target point score information set;
the processing module is used for obtaining an intersection target point information set and an intersection disease target point score information set according to the drug action target point information set, the disease target point information set and the disease target point score information set;
and the analysis module is used for analyzing according to the compound information, the intersection target point information set, the intersection disease target point score information set and a preset target point interaction matrix by a preset algorithm to obtain an analysis result.
9. An electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized by: the processor, when executing the computer program, implements the chinese medicine formulation and disease analysis method as defined in any one of claims 1 to 7.
10. A computer-readable storage medium storing computer-executable instructions for causing a computer to execute the chinese medicine prescription and disease analysis method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110694041.5A CN113486231B (en) | 2021-06-22 | 2021-06-22 | Chinese medicinal prescription and disease analysis method, analysis system, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110694041.5A CN113486231B (en) | 2021-06-22 | 2021-06-22 | Chinese medicinal prescription and disease analysis method, analysis system, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113486231A CN113486231A (en) | 2021-10-08 |
| CN113486231B true CN113486231B (en) | 2023-08-08 |
Family
ID=77935621
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110694041.5A Active CN113486231B (en) | 2021-06-22 | 2021-06-22 | Chinese medicinal prescription and disease analysis method, analysis system, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113486231B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110648726A (en) * | 2019-09-23 | 2020-01-03 | 清华大学 | Network target-based drug network pharmacology intelligent and quantitative analysis method and system |
| CN110890136A (en) * | 2019-11-13 | 2020-03-17 | 安徽中医药大学 | Traditional Chinese medicine pharmacological analysis method |
| CN112735534A (en) * | 2021-01-25 | 2021-04-30 | 天津中医药大学 | Traditional Chinese medicine network pharmacology deep analysis method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11037684B2 (en) * | 2014-11-14 | 2021-06-15 | International Business Machines Corporation | Generating drug repositioning hypotheses based on integrating multiple aspects of drug similarity and disease similarity |
-
2021
- 2021-06-22 CN CN202110694041.5A patent/CN113486231B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110648726A (en) * | 2019-09-23 | 2020-01-03 | 清华大学 | Network target-based drug network pharmacology intelligent and quantitative analysis method and system |
| CN110890136A (en) * | 2019-11-13 | 2020-03-17 | 安徽中医药大学 | Traditional Chinese medicine pharmacological analysis method |
| CN112735534A (en) * | 2021-01-25 | 2021-04-30 | 天津中医药大学 | Traditional Chinese medicine network pharmacology deep analysis method and system |
Non-Patent Citations (1)
| Title |
|---|
| 庞晓丛 ; 王喆 ; 方坚松 ; 连雯雯 ; 赵赢 ; 康德 ; 刘艾林 ; 杜冠华.治疗阿尔茨海默病的中药有效成分的网络药理学研究.世界中医药大会第三届夏季峰会暨大健康博览会.2017,725-731. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113486231A (en) | 2021-10-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhang et al. | DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug–Target interaction prediction | |
| Liu et al. | A maximum pseudo-likelihood approach for estimating species trees under the coalescent model | |
| CN113012770B (en) | Multi-modal deep neural network based prediction of drug-drug interaction events | |
| CN109493925B (en) | Method for determining incidence relation between medicine and medicine target | |
| CN104199855B (en) | A kind of searching system and method for traditional Chinese medicine and pharmacy information | |
| Zhang et al. | Integrating multiple protein-protein interaction networks to prioritize disease genes: a Bayesian regression approach | |
| Song et al. | Productivity and influence in bioinformatics: A bibliometric analysis using PubMed central | |
| CN103093108A (en) | Analysis platform and analysis method of pharmacology of traditional Chinese medicine system | |
| CN110706755A (en) | Tubercle bacillus drug resistance detection method, device, computer equipment and storage medium | |
| CN105224823A (en) | A kind of drug gene target spot Forecasting Methodology | |
| CN113611372B (en) | Method and system for predicting drug effect fitting of effective components of traditional Chinese medicine prescription | |
| CN113486231B (en) | Chinese medicinal prescription and disease analysis method, analysis system, equipment and storage medium | |
| CN117789857A (en) | Traditional Chinese medicine formula prediction method and system based on molecular dynamic synergy | |
| Arshad et al. | An explainable stacking-based approach for accelerating the prediction of antidiabetic peptides | |
| CN110534153B (en) | Target prediction system and method based on deep learning | |
| WO2015013367A1 (en) | Method of identifying new uses of known drugs | |
| CN115691751A (en) | Traditional Chinese medicine prescription screening method and system based on diagnosis and treatment experience and intelligent learning | |
| CN114822681A (en) | Virus-drug association prediction method based on recommendation system | |
| CN115410645A (en) | Method for identifying action target of Chinese patent medicine for treating new coronary pneumonia | |
| Schuler et al. | Foundations for a Realism-based Drug Repurposing Ontology. | |
| Jiang et al. | MOASL: Predicting drug mechanism of actions through similarity learning with transcriptomic signature | |
| Menke et al. | Wrap-and-pack: a new paradigm for beta structural motif recognition with application to recognizing beta trefoils | |
| Rost et al. | Evolution and neural networks/spl minus/protein secondary structure prediction above 71% accuracy | |
| CN115938609B (en) | Drug repositioning method and system integrating curative effect comparison information | |
| CN115116561B (en) | Application of drug-target protein-schizophrenia interaction network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |