CN107169310A - A kind of genetic test construction of knowledge base method and system - Google Patents
A kind of genetic test construction of knowledge base method and system Download PDFInfo
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Abstract
The present invention relates to a kind of genetic test construction of knowledge base method and system, including：Build database entity table, the contingency table of public database and the contingency table of text mining；Build contingency table scoring system；Build database matching management system；The present invention carries out knowledge base by the way of semi-automation is semi-artificial and built, both the deviation manually built and careless omission were considered, it is contemplated that the confidence level difference of public database, during text mining algorithm and public database Information Gathering Software are incorporated the step of automatically collecting, devise core library and candidate storehouse, and the entry for having verification storehouse to obscure score value carries out desk checking, the mode automatically updated is employed to be managed data-base content, the knowledge base management system of matching is designed, is easy to carry out database content search, verification, modification and version management.
Description
Technical field
The present invention relates to biomolecule information database field, and in particular to a kind of genetic test construction of knowledge base method and is
System.
Background technology
The development of two generation sequencing technologies so that the mankind increasingly can easily obtain most basic genome sequence,
Solve the hereditary information of itself.It is many scientific investigations showed that, many diseases of the mankind, phenotype and the reaction to medicine all originate from individual
Genetic background difference, that is, each human DNA sequence difference.It is more and more since the Human Genome Project in 2000
Human genome be decrypted, these compositions of genome reference sequences of human genome.The direct purpose of genetic test is,
(or subregion is sequenced) is sequenced by the genome to individual, the sequence of itself and reference gene group DNA level is obtained
Row difference, then again by the comparison with existing knowledge base, come predict possible disease, phenotype, drug response association.
Mutant dna sequence and disease, phenotype, the source of drug response related information typically have：
GWAS(Genome-wide association study)：If thering is specified disease, phenotype and medicine to make some
Crowd carries out hereditary information excavation, finds the difference of they and reference sequences, then may infer that, it may be possible to these DNA
The difference of level, has ultimately resulted in the differential responses of disease, phenotype either to medicine.
Biochemistry, genetics experiments in traditional biological study：It can also be looked for by traditional biological study method
It is to a large amount of and disease, phenotype either to genetic mutation, protein variations, enzyme variation of drug response etc., these variations are anti-
DNA aspects are reflected, are the generations of class variation in DNA level.Such as, a disease is due to the trans-membrane region of a protein
It there occurs that variation causes the exception of ion channel, then, encode in this protein DNA sequence, as long as any have DNA water
Flat variation have impact on the transmembrane domain of protein, it is possible to cause the generation of this disease.Part Experiment is in pattern life
Thing and cell line are completed.
Other：Pass through gene, protein expression amount difference in study of disease sample and check sample；It is biological by calculating
Method carries out function prediction etc..
More than, the difference of source of evidence, confidence level also can be different, and the element of the association such as disease is also differed.It is applied to
The sequence variations of DNA level and the relation of disease, it may be desirable to which different record and subsequent treatment modes are, it is necessary to design and put up
Knowledge base.
It is to build the core link in GENE Assay analysis flow that knowledge base, which is built,.But present knowledge base exist it is following
Problem：
Building a knowledge base needs labor intensive material resources, generally requires integration public database, adds professional and read
Scientific documents are read to complete.
Public database level is uneven, and artificially collect always there is certain deviation.
Many knowledge bases can obscure the information of the different aspects such as DNA, RNA, protein, such as GWAS is to be directed to DNA layers
The result in face, and the research of some diseases related genes is some researchs related to drug metabolism in protein aspect
It is in enzyme and metabolin aspect, although be eventually grouped into the variation of DNA aspects, but during database building, it should
Make a distinction in advance, so that subsequent algorithm is developed.
Based on a series of algorithm such as text mining, knowledge base is built automatically, but fully rely on the knowledge of algorithm structure
Storehouse false positive rate and false negative rate are all very high, have a great impact to follow-up genetic test disease risks analysis.
Many knowledge bases are built after completion, some existing associated entries be also required to according to new scientific achievement it is automatic more
Newly, but due to the structure design of these databases solidification, automatically update difficult to realize..
Many knowledge bases can all use some public database management languages, not square to the researcher of Biological background
Just use.
Biological information Relational database in existing patent of invention examination ＆ verification, is contained：A kind of bioinformatic database system
System and data processing method (application number：201410009130.1) and biomolecule information database construction method and device (application
Number：201410742604.3)
Wherein, in first invention, a kind of biomolecule information database system and data processing method are disclosed, it is possible to achieve
The unified management of bioinformatic data.Sample, project and experiment module are contained in the system, main purpose is convenient experiment
Design and data processing, improve operating efficiency；In second invention, a kind of method for building biomolecule information database is disclosed
And device, mainly by the text mining made a summary to PubMed, to disease, related literature summary is decomposed, according to mutation
Regular expression semantic base carries out the extraction and classification of gene and abrupt information, builds the related semantic base of disease, determines gene
The score of mutation, finally builds biomolecule information database.
The content of the invention
For deficiency of the prior art, it is an object of the invention to provide a kind of genetic test construction of knowledge base method and it is
System, including：Genetic test relevant knowledge storehouse building method is provided, and after database building completion, based on database
Management system.
The purpose of the present invention is realized using following technical proposals：
The present invention provides a kind of genetic test construction of knowledge base method, and it is theed improvement is that, methods described includes：
Build database entity table, the contingency table of public database and the contingency table of text mining；
Build contingency table scoring system；
Database matching management system is built, is easy to expert to search, verifies, changes contingency table.
Further, in the structure database entity table, the contingency table of public database and the contingency table of text mining
Before, in addition to：Collect public database and it is arranged, and database structure is determined according to public database.
Further, the structure database entity table, including the cdna sample containing disease, phenotype and environmental factor form
Information；
The environmental factor form is manual entry form；
In the match information between integrating disparate databases ID, for biology element, take and navigate to genome position
Put, judge whether intersecting for position is more than the 0.5 of union to judge；
For the title of disease, then using the matching form of database typing itself, if unmatched, record is abandoned
Enter.
Further, the contingency table for building public database, including：
Public database is layered；
Given a mark according to layering level, marking scope, -1~1, is positive correlation more than 0, be negative correlation less than 0.
Further, the contingency table for building text mining, including：
Collect the literature summary within nearly 25 years；
Literature summary is classified；
Evidence rank is given a mark, marking is included：Verb list reaches the marking of positive-negative relationship；Different document score values is carried out
Superposition, and absolute value is added, if the score value being directly added is less than threshold value, the literature summary of evidence level is extracted and is put into school
Test table；Final score maximum is 1 point, minimum -1 point.
Further, contingency table scoring system is built according to the contingency table of public database and the contingency table of text mining,
Including：
Integrate public database and excavate the score value that obtained score value and literature mining are obtained；
Last associated entry comes from above-mentioned two source, and all associated entries are carried out according to relationship score to collect row
Sequence, sets threshold value, is respectively put into core contingency table and candidate association table；
If the entry in public database source and the entry score value difference of literature reference are more than threshold value, verification is put into
Table；
There is the associated entry for determining OR values for disease, if there is no conflicting or OR value fold differences 0.5~2
Within, then directly enter core contingency table；If it is present being put into checklist.
Further, the database matching management system is checked for database, database search, database are verified,
Database version management, log query pages and the operation standard page.
Further, after the structure database matching management system, in addition to：The artificial school of database checklist
Test；Verifying work will carry out entry in data base management system and browse and manual modification.
Total described, the invention provides a kind of genetic test construction of knowledge base system, it is theed improvement is that, the system
System includes：
First builds module：Association for building database entity table, the contingency table of public database and text mining
Table；
Second builds module：For building contingency table scoring system；
3rd builds module：For building database matching management system.
Further, the system also includes collecting public database and it is arranged, and is determined according to public database
The collection module of database structure.
Compared with immediate prior art, the beneficial effect that the technical scheme that the present invention is provided reaches is：
1. carrying out knowledge base by the way of semi-automation is semi-artificial to build, the deviation manually built both is considered and has dredged
Leakage, it is contemplated that the confidence level difference of public database.
2. the mode automatically updated is employed to be managed data-base content.
3. in incorporating text mining algorithm and public database Information Gathering Software the step of automatically collecting.
4. devising core library and candidate storehouse, and there is verification storehouse to carry out desk checking to the entry that score value is obscured.
5. devising the knowledge base management system of matching, it is easy to carry out database content search, verification, modification and version
Management.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the accompanying drawing used required in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is a kind of flow chart of genetic test construction of knowledge base method；
Fig. 2 is knowledge data base structural representation.
Embodiment
To make the object, technical solutions and advantages of the present invention clearer, technical scheme will be carried out below
Detailed description.Obviously, described embodiment is only a part of embodiment of the invention, rather than whole embodiments.Base
Embodiment in the present invention, those of ordinary skill in the art are resulting on the premise of creative work is not made to be owned
Other embodiment, belongs to the scope that the present invention is protected.
First optimal technical scheme
The form of knowledge base is generally divided into two major classes, and a class is entity table, and a class is contingency table.Entity table is exactly to data
Element and the record of element essential information in storehouse.Member in general biological species knowledge base have：Sequence difference, the base of DNA level
Cause, transcript, protein, metabolin, environmental factor, disease, phenotype, medicine etc..The sequence difference of DNA level can be according to it
Complexity, is divided into：
*SNP(single nucleotide polymorphism)：Most common DNA sequence dna difference, is the prominent of single base
Become, account for more than the 90% of known dna polymorphism, it is widely distributed, study relatively more.According to ACMG guides, typically according to dbSNP numbers
Specification is carried out to SNP according to the standard in storehouse.Frequencies of the SNP in crowd, is also critically important information in Risk Calculation, typically adopts
With 1000Genomes and Hapmap data.
*indel(insertion and deletion)：The insertion and deletion of small fragment, typically the length model in 2~16bp
Enclose, not over 1kb.Name is also according to dbSNP databases.
*CNV(copy number variation)：Copy number variation, minimum 50bp length, it is most of 1kb~
3Mb, can include the CNV of large fragment.Its subclass has DEL (deletion), DUP (duplication), INS
And LCV (large scale CNV) (insertion).The general dbVar database standards recommended according to ACMG, if do not deposited
In entry, then using DGV databases.
*SV(structure variation)：The structure variation of large fragment, SV contains CNV in the definition of broad sense, is
Difference, herein SV only include INV (inversion) and translocation, the displacement of chromosome and transposition.It is typically also
The dbVar database standards recommended according to ACMG, if there is no entry, then using DGV databases.
If standardization input mode can separately be had by being not present in data above storehouse in matching entry, the present invention.
Gene, transcript, protein reference RefSeq databases, if it does not match, using the ENSG in Ensembl
(ENST, ENSP) is named, and can record Official Gene Symbol, to do text mining.
Enzyme and metabolin are then according to the annotation of KEGG databases.
The name of disease, the main omim database recommended using ACMG, add ICD10, MalaCard, MedGen,
Orphanet databases.
The name of phenotype, mainly using HPO (human phenotype ontology) annotation, and MeSH, UMLS number
According to storehouse.
The name of medicine, it is main to use DrugBank databases.
More than, it is the entity table of knowledge base.
Contingency table is the record in database, being associated between element.It is the association between biology element first：
* in mutant dna sequence, the linkage disequilibrium (LD, the linkage that are associated as between them between SNP
disequilibrium).SNP independence analysis in being calculated for disease risks.According to 1000GenomesProject data
The information in storehouse.
* gene->Transcript->Between protein (enzyme), according to the central dogma of transcription, translation, it is connected between element.With
In influence of the prediction mutant dna sequence to coded protein, then the disease association with albumen qualitative correlation.Mainly according to RefSeq
Database, the influence on DNA sequence dna to coded protein, then using Annovar algorithms.
* between gene-gene, mainly according to the information of genetic interaction and coevolution (Biogrid,
MINT)；Between protein, mainly according to the interactive network (Biogrid, MINT) of protein；Protein (enzyme), metabolin
Between, mainly according to KEGG metabolite datas storehouse.These dependency relations serve primarily in the explanation to mutation, and with disease (table
Type, drug response) between Relationship Prediction.
* the relation of mutant dna sequence and disease, mainly according to ClinVar, HGMD, DECIPHER database；Gene (egg
White matter) with the relation of disease, mainly according to databases such as OMIM, UniProt, GAD, DisGeNET；
* the relation of mutant dna sequence/genes/proteins matter and phenotype, mainly according to HPO databases；
* mutant dna sequence/genes/proteins matter and the relation of medicine, mainly according to data such as DrugBank, PharmGKB
Storehouse.
Other, such as relation of environmental factor and disease etc., not ready-made public database, these are all by right
PubMed carries out text mining acquisition.
Semi-automatic, semi-artificial mode is taken to build knowledge base
Knowledge base is designed using module layer-stepping.The entity of database includes：According to the element of biology, be divided into DNA,
RNA, protein, enzyme and metabolin, five aspects of environmental factor, according to research purpose, are divided into disease, phenotype, medicine etc. three big
Module.Element in each aspect and module, all can be preferentially by collecting ACMG (The American College of
Medical Genetics and Genomics) information in generally acknowledged public database, build the entity that knowledge base needs
Table；Special evidence storehouse is designed in addition, records the source-information of each record of knowledge base.
For the contingency table between entity, it will carry out primary dcreening operation to the public database of required digging evidence by expert, and it is right
The confidence level of each database is scored.Then, software is collected by database automatically, related information is acquired, and led to
Evidence assessment algorithm program is crossed, the confidence level scoring for providing every evidence (incorporates database confidence level, data base entries credible
The intersection degree of degree and disparate databases record), score value is normalized to -1~1.
* the false positive and false negative of public database information are considered, the present invention is contained for PubMed data in literature
The text in storehouse is dug according to algorithm.The thought of algorithm design is to download the newest full storehouses of summary of PubMed, first according to keyword by every
Content in document and three big modules is matched, and document and the contingency table of disease (phenotype, medicine) is built, then by five layers
All elements in face are matched in the related literature summary of each disease (phenotype, medicine), and association is carried out according to semanteme
(uncorrelated) and scoring (positive correlation, negative correlation) are filtered, for OR value of the complex disease if determination, source and OR is recorded
It is worth sentence.The scoring of different documents is collected again, and is corrected with all document numbers, score value is normalized to -1~1.
Last associated entry comes from above-mentioned two source, and all entries are carried out according to relationship score to collect sequence,
Threshold value is set, core contingency table and candidate association table is respectively put into；If the entry and literature reference in public database source
Entry has greater difference (score value difference is more than 0.5, and threshold value can adjust), then is put into checklist.Wherein, have really for complex disease
Determine the associated entry of OR values, (OR value fold differences are 0.5~2 if there is no associated entry that is conflicting or differing greatly
Within, threshold value is adjustable), then core contingency table is directly entered, if it is present being put into checklist.
Human expert is transferred to be handled checklist content.Associated entry is respectively put into by core contingency table according to result
With candidate association table.
Knowledge base can monthly automatically update (update cycle is adjustable), and following work will be repeated every time by updating：Judge
Public database updates, if so, being given a mark again to entry；The newest full storehouses of summary of PubMed are updated, algorithm above is repeated, it is right
Entry is given a mark；According to result, associated entry is reentered into core contingency table, candidate association table, but if entry
Table ownership is changed, it will be put into checklist, desk checking is carried out by special messenger.
Make in order that the biology direction expert without database specialty background carries out desk checking and inquiry to database
With the present invention specially develops supporting data base management system, contains the functions such as inquiry, modification, log records, can make
Expert in terms of biology is verified in time to data-base content.In addition, database meeting is according to whether there is modification schedule backup,
Also it can be carried out determining version according to research and development of products progress, it is ensured that the database used in production environment can trace to the source.
Second optimal technical scheme
The present invention provides a kind of genetic test construction of knowledge base method, and its flow chart is as shown in figure 1, comprise the steps：
1st, collection and edit of the first step for public database, the database collected now has：dbSNP、
1000GenomeProjects、dbVar、DGV、RefSeq、ENSG、ClinVar、HGMD、DECIPHER、OMIM、HPO、MeSH、
UMLS、DrugBank、Biogrid、MINT、UniProt、GAD、DisGeNET、PharmGKB、KEGG、PubMed
Abstract.Wherein except PubMed Abstract, the initial data of other databases is all processed into tab formatted files, form
It is as follows：
2. second step is knowledge data base structure design, database structure such as Fig. 2.In each module, there is special messenger's input public
The title and major key of database, pass through the software of independent development, the initial integration tab formatted files in automatically generated data storehouse altogether.
Enumerate in fig. 2 preparation in need file name, and file association source.Now, secondary file is also had, is come
The information such as the ID matchings between storage disparate databases.
3. the 3rd step is the structure of entity table in database, entity table contains Snp (based on dbSNP), Var (except
SNP mutant dna sequence, based on dbVar), Gene (contain the information of transcript, protein and enzyme, using RefSeq as
It is main), Env (environmental factor form), Disease (disease information, based on OMIM and ICD10 databases), Pheno (believe by phenotype
Breath, based on HPO) and Drug (medicine, based on DrugBank).Wherein, environmental factor form is artificial rule of thumb typing.
In the match information between integrating disparate databases ID, for biology element, take and navigate to genomic locations, then sentence
Whether about what positioning was put intersects the 0.5 of union to judge；For the title of disease, then using the typing of the databases such as OMIM itself
Matching form, the disease name in addition to OMIM, Orphanet, ICD10 in other diseases information database, if matching
On not, then typing is abandoned.
4. the 4th step builds for the contingency table based on public database.Association evidence is beaten by the software of independent development
Point (be positive correlation more than 0 scope -1~1, be negative correlation less than 0, most of data-base recording positive correlation information).Marking
Method be：
* database is classified, given a mark, by taking the association of disease and gene as an example, database is divided into two levels,
UniProt, OMIM are first layer, and GAD, DisGeNet are the second layer, and the score value of first layer is set to 0.3 point, the score value of the second layer
For 0.1 point, if the association in legacy data storehouse has had score value, normalized to after 0~1, and database is in itself
Score value is multiplied.Then the identical recordings score value of disparate databases is overlapped, and highest is divided into 1 point.
* database, can be all layered by each contingency table in advance by expert.
5. the 5th step builds for the contingency table based on text mining.The api interfaces provided first using PubMed official websites, under
The summary of all documents since load nineteen ninety.Using independent development software carry out text mining (based on python programming,
Nltk natural language processings bag).Basic program thread is as follows：
* abstract files (plain text format) are read in, the annotation of MH (MESH databases) is determined whether there is, if so,
Then extracted, and all entity tables (and all alias tables) are compared in database；If not provided, directly to summary
(AB) participle is carried out, then by all participles, carries out part of speech classification, by all nouns and all entity tables (and all alias
Table) it is compared.Now main and disease_info, disease_alias form compares (drug, pheno are similar).This step
Main purpose be that literature summary is classified and (allows a document to be grouped into multiple classifications).
* for the variation of DNA sequence dna level, in Snp_alias.txt files, SNP with No. RS be topmost major key,
And HGVS titles are collected, according to rsID (e.g rs1024323)>Base changes (e.g NC_000004.11:g.3006043C>
T)>Transcript changes (e.g NM_001004056.1:c.329C>T)>Amino acid changes (e.g NP_001004057.1:
P.Ala142Val, NP_001004057.1:p.A142V)>Position (e.g 37:chr4；3006043；C；T), and plus now
Gene common first names GRK4.If directly occurring in that rs1024323 in sentence, or occur GKR4 and A142V simultaneously, it is as follows：
e.g:We hypothesized that 3nonsynonymous GRK4single-nucleotide
Polymorphisms, R65L (rs2960306), A142V (rs1024323), and A486V (rs1801058), would be
Associated with blood pressure response to atenolol, but not
Hydrochlorothiazide, and would be associated with long-term cardiovascular
Outcomes (all-cause death, nonfatal myocardial infarction, nonfatal stroke) in
participants treated with an atenolol-based versus verapamil-SR-based
antihypertensive strategy.
e.g:METHODS:Participants from the African American Study of Kidney
Disease and Hypertension(AASK)trial were genotyped at three
GRK4polymorphisms:R65L, A142V, and A486V.
Then corresponding sentence is proposed.
* for the document in same category, Atenolol (atenolol) for example now, more than in first case
Obtain it is related to A142V, then in whole summary, extract the related sentence of description " A142V (rs1024323),
... would be associated with blood pressure response to atenolol ", now pass through
Participle, obtains verb therein and passes judgement on emotional semantic classification, the relation of record mutation and medicine, and whole sentence.
* now evidence rank can also be given a mark, marking is included：Verb list reaches the marking of positive-negative relationship；It is such as clear and definite
Expression related " associated with " give 0.3 point, if " might " waits the vocabulary of reduction dependency relation, then to 0.1 point.
It is related if negative sense, then to -0.3 and -0.1 point.Different document score values are overlapped, and absolute value is added, if directly
The score value for connecing addition is less than the score value (threshold value is adjustable) that 0.5* absolute values are added, then extracts the record, be put into checklist.Most
Whole score maximum is 1 point, minimum -1 point.
6. the 6th step is contingency table scoring system.Main purpose is to integrate public database to excavate obtained score value and document
Excavate obtained score value.Last associated entry comes from above-mentioned two source, and all entries are converged according to relationship score
Total sequence, sets threshold value, is respectively put into core contingency table and candidate association table；If the entry and document in public database source
The entry in source has greater difference (score value difference is more than 0.5, and threshold value can adjust), then is put into checklist.Wherein, for complicated disease
Disease has the associated entry for determining OR values, and if there is no associated entry that is conflicting or differing greatly, (OR value fold differences exist
Within 0.5~2, threshold value is adjustable), then core contingency table is directly entered, if it is present being put into checklist.
7. the 7th step is built for the data base management system of matching, the code of the system is finished writing in advance, and original date storehouse
Structure matching, can carry out secondary development according to database real demand.The hardware configuration that corollary system needs is as follows：
(1) database part is developed using MySql.
(2) managing web part is developed using Django, for database data search, modification etc., managing institute
There are html pages etc..
(3) page is write using html5, and comprising css, javascript code definition style sheets write the page complicated
Action etc..
(4) system is backed up using Linux Shell scripts.
System building is in Linux centos platforms, 4 core CPU, 4G internal memories, 1T hard disks.
8. the 8th step is the desk checking of database checklist.This step verifying work will in a management system, R＆D expert
Carry out after Account Logon, carry out entry and browse and manual modification.
9. the 9th step is the test of match management system.Management system is contained：Database is checked, database search, number
Verified according to storehouse, database version management, log query pages and the operation standard page.
The data for the construction method database that the present invention is provided also directly pass through common data from not only literature summary
Storehouse is excavated, and the information excavated is not limited to disease, further comprises phenotype, environmental factor etc., more comprehensively.In addition, this hair
In bright, the management system of database matching is contained.
3rd optimal technical scheme
Based on same inventive concept, the present invention also provides a kind of genetic test construction of knowledge base system, including：
First builds module：Association for building database entity table, the contingency table of public database and text mining
Table；
Second builds module：For building contingency table scoring system；
3rd builds module：For building database matching management system.
The system also includes collecting public database and it is arranged, and determines database structure according to public database
Collection module.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any
Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, should all be contained
Cover within protection scope of the present invention.Therefore, protection scope of the present invention should be based on the protection scope of the described claims.
Claims (10)
1. a kind of genetic test construction of knowledge base method, it is characterised in that methods described includes：
Build database entity table, the contingency table of public database and the contingency table of text mining；
Build contingency table scoring system；
Database matching management system is built, is easy to expert to search, verifies, changes contingency table.
2. genetic test construction of knowledge base method as claimed in claim 1, it is characterised in that in the structure database entity
Before the contingency table of table, the contingency table of public database and text mining, in addition to：Collect public database and it arranged,
And database structure is determined according to public database.
3. genetic test construction of knowledge base method as claimed in claim 1, it is characterised in that the structure database entity
Table, including the cdna sample information containing disease, phenotype and environmental factor form；
The environmental factor form is manual entry form；
In the match information between integrating disparate databases ID, for biology element, take and navigate to genomic locations, sentence
Position whether the intersection put is more than the 0.5 of union to judge；
For the title of disease, then using the matching form of database typing itself, if unmatched, typing is abandoned.
4. genetic test construction of knowledge base method as claimed in claim 1, it is characterised in that the structure public database
Contingency table, including：
Public database is layered；
Given a mark according to layering level, marking scope, -1~1, is positive correlation more than 0, be negative correlation less than 0.
5. genetic test construction of knowledge base method as claimed in claim 1, it is characterised in that the pass of the structure text mining
Join table, including：
Collect the literature summary within nearly 25 years；
Literature summary is classified；
Evidence rank is given a mark, marking is included：Verb list reaches the marking of positive-negative relationship；Different document score values are folded
Plus, and absolute value is added, if the score value being directly added is less than threshold value, the literature summary of evidence level is extracted and is put into verification
Table；Final score maximum is 1 point, minimum -1 point.
6. genetic test construction of knowledge base method as claimed in claim 1, it is characterised in that according to the association of public database
Table and the contingency table of text mining build contingency table scoring system, including：
Integrate public database and excavate the score value that obtained score value and literature mining are obtained；
Last associated entry comes from above-mentioned two source, and all associated entries are carried out according to relationship score to collect sequence,
Threshold value is set, core contingency table and candidate association table is respectively put into；
If the entry in public database source and the entry score value difference of literature reference are more than threshold value, checklist is put into；
For disease have determine OR values associated entry, if there is no conflicting or OR values fold difference 0.5~2 it
It is interior, then directly enter core contingency table；If it is present being put into checklist.
7. genetic test construction of knowledge base method as claimed in claim 1, it is characterised in that the database matching management system
Unite checked for database, database search, database verification, database version management, log query pages and operation standard page
Face.
8. genetic test construction of knowledge base method as claimed in claim 1, it is characterised in that in the structure database matching
After management system, in addition to：The desk checking of database checklist；Verifying work will carry out bar in data base management system
Mesh is browsed and manual modification.
9. a kind of genetic test construction of knowledge base system, it is characterised in that the system includes：
First builds module：Contingency table for building database entity table, the contingency table of public database and text mining；
Second builds module：For building contingency table scoring system；
3rd builds module：For building database matching management system.
10. genetic test construction of knowledge base system as claimed in claim 1, it is characterised in that the system also includes collecting
Public database is simultaneously arranged to it, and determines according to public database the collection module of database structure.
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