CN116189897B - Virus cancer risk prediction method and system based on time sequence change relation - Google Patents
Virus cancer risk prediction method and system based on time sequence change relation Download PDFInfo
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Abstract
The invention discloses a virus cancer risk prediction method and a virus cancer risk prediction system based on a time sequence change relation. The latest and most comprehensive analysis of tumor immunity infiltration and various immunity infiltration algorithms and visual presentation of virus positive groups and virus negative groups in cancer patients can be carried out by a user through simple clicking, so that the further development of the tumor virology direction immunity treatment field is greatly promoted, and the value of the current data resources is released.
Description
Technical Field
The invention belongs to the field of biological gene and data visualization, and particularly relates to a method and a system for predicting viral cancer risk based on a time sequence change relation.
Background
Chronic HBV infection is one of the major risk factors for primary hepatocellular carcinoma worldwide. High-risk HPV infection is a primary factor in cervical lesions and cervical cancer, and moreover, HPV infection is gradually becoming a major causative factor for head and neck tumors and other tumors. EBV infection is associated with the development and progression of nasopharyngeal carcinoma, lymphoma and gastric cancer. MCPyV infection is closely related to the occurrence of a rare fatal MCC. With the progressive maturation and use of inhibitors of tumor immunotherapy, such as programmed death protein-1 (programmed death protein-1, PD-1) and its ligands (programmed death protein-li-bind 1, PD-L1), the close association of tumors with the therapeutic efficacy and prognosis of immunotherapy is becoming more and more important.
Studies have shown that specific viral infections are closely related to the development of tumorigenesis. High throughput sequencing and microarray technology have led researchers to obtain a large amount of expression data for patients with tumors associated with viral infections. Thus, these expression data provide a theoretical basis for exploring the oncogenic mechanisms of viruses for tumor therapy. In recent years, studies have found that viral infection may be correlated with tumor cells and tumor cells of the tumor immune microenvironment. However, tumor immune microenvironment analysis regarding the direction of tumor associated with viral infection is not currently addressed by existing web tools.
Disclosure of Invention
According to a first aspect of the present invention, the present invention claims a method for predicting the risk of viral cancer based on a time-series variation relationship, the method comprising:
the risk prediction system senses a differential query request and comprises a first prediction condition, wherein the first prediction condition is a genetic immunity prediction condition;
the risk prediction system responds to the difference query request and queries a first field of a first prediction condition in a first input frame of a query form of the risk prediction system, wherein the first field is positive for virus of the first prediction condition;
the query form comprises at least two input boxes, wherein the at least two input boxes comprise a first input box and a second input box, and indexes are arranged between different input boxes;
the risk prediction system perceives a second query request on the first field, the second query request is used for opening a second field of the first prediction condition, the second field corresponds to a different database GSE sample set with the first field, the second field is a gene sequence list field, and the second field comprises at least one index of a gene sequence;
The risk prediction system queries the second field in the second input box in response to the second query request;
the risk prediction system perceives a query request on the second field triggering an index of a first genetic sequence belonging to one of the indexes of the at least one genetic sequence;
the risk prediction system responds to the query request triggering the index of the first gene sequence, queries a gene signal path of the first gene sequence in the second input frame, and updates the first field queried in the first input frame to the second field, wherein the gene signal path of the first gene sequence corresponds to a different GSE sample set with the second field.
Further, after querying a gene signal pathway of the first gene sequence within the second input box and updating the first field queried within the first input box to the second field, the method further comprises:
the risk prediction system perceives a query request on the second field triggering an index of a second genetic sequence belonging to one of the indexes of the at least one genetic sequence;
The risk prediction system responds to the query request triggering the index of a second gene sequence, queries a gene signal path of the second gene sequence in the second input frame, and still queries the second field in the first input frame, wherein the gene signal path of the second gene sequence corresponds to a different GSE sample set with the second field;
the difference query request is a query request for opening the first prediction condition;
the first input frame is positioned above the second input frame;
the number of the query immune infiltration algorithm of the first field when the first input box is queried and the number of the query immune infiltration algorithm of the second field when the second input box is queried are multiple.
Further, the second field is a list field queried by a user after searching the first field for the gene sequence;
the risk prediction system stores a query scheme corresponding to the first prediction condition; when the risk prediction system runs the first prediction condition, inquiring each field of the first prediction condition according to an inquiry scheme corresponding to the first prediction condition;
the risk prediction system, in response to the differential query request, queries a first field of a first prediction condition within a first input box of a query form of the risk prediction system, including:
The risk prediction system responds to the difference query request and queries the first field of the first prediction condition in the first input box of a query form of the risk prediction system in a heat map query state;
the second field has a lower level than the first field;
the level of the field corresponding to the gene signal pathway of the first gene sequence is lower than the level of the second field.
Further, when the risk prediction system queries a first field of a first prediction condition in a first input box of a query form of the risk prediction system in response to the differential query request, not querying a field of the first prediction condition in the second input box;
the absolute value of the difference value between the thermodynamic diagram coordinate scale of each input frame and the preset proportion is smaller than or equal to a first preset value, and the thermodynamic diagram coordinate scale is the ratio of the ordinate to the abscissa of the input frame in the thermodynamic diagram inquiry state;
when the risk prediction system queries the second field in the second input box in response to the second query request, the first field is still queried in the first input box;
the heat map query state is: the degree of correlation between the immune cell abundance field of the query form and the gene expression quantity and the signal path score is smaller than or equal to a second preset value;
Before the risk prediction system perceives the differential query request, the method further comprises:
the risk prediction system queries a desktop in a heat map query state;
the difference query request is a query request for opening the first prediction condition;
before the risk prediction system perceives the differential query request, the method further comprises:
the risk prediction system queries the first field in a correlation query state;
the difference query request is a query request switched from the correlation query state to a heat map query state;
the risk prediction system queries a first field of a first prediction condition within a first input box of a lookup table of the risk prediction system, comprising:
if the first prediction condition does not support heat map query, the risk prediction system queries a first field of the first prediction condition in a first input box of the query form;
if the first prediction condition supports heat map query, the risk prediction system prompts a user to select a query mode, wherein the query mode comprises a heat map query mode and an input box query mode; in response to a user selecting the input box query, the risk prediction system queries a first field of the first prediction condition within a first input box of the query form.
Further, the method further comprises:
if the risk prediction system is switched to a correlation query state, the risk prediction system queries a field of the first prediction condition in the correlation query state;
wherein, the correlation inquiry state is: the degree of correlation between the immune cell abundance field and the gene expression quantity and the signal path score of the query form is greater than a second preset value;
the field of the first prediction condition of the query is the field of the latest query of the risk prediction system in a heat map query state;
or, the field of the first prediction condition of the query is a field in which the risk prediction system senses the user query request at the latest in a heat map query state;
or, the field of the first prediction condition of the query is the field of the latest query in a preset input frame of the risk prediction system in the heat map query state;
querying a first field of a first prediction condition within a first input box of a lookup table of the risk prediction system includes:
querying the first field of the first prediction condition in the first input frame after querying the tumor associated data of the first prediction condition in the first input frame;
The risk prediction system perceives a query request on a first field of the first prediction condition for indicating to open a second prediction condition, and queries a field of the second prediction condition in the second input box; the cancer element character string queried in the first input frame is unchanged;
the difference query request is a gesture query request or a voice query request of a user;
the second query request is a trigger query request on the first field, the method further comprising:
responding to the second query request, wherein the query effect of the triggered cancer element character string on the first field is changed;
one or more of the data source, length, immune infiltration algorithm, pattern type, or query form of the triggered cancer element string changes.
According to a second aspect of the present invention, the present invention claims a viral cancer risk prediction system based on a time series variation relationship, comprising a lookup table, a memory, one or more processors, a first prediction condition, and one or more programs; wherein the one or more programs are stored in the memory; the one or more processors, when executing the one or more programs, cause the risk prediction system to perform the method of:
Sensing a difference query request;
responding to the difference query request, and querying a first field of the first prediction condition in a first input frame of a query form of the risk prediction system, wherein the first field is virus negative positive of the first prediction condition, and the first prediction condition is a genetic immunity prediction condition; the query form comprises at least two input boxes, wherein the at least two input boxes comprise a first input box and a second input box, and indexes are arranged between different input boxes;
sensing a second query request on the first field, wherein the second query request is used for opening a second field of the first prediction condition, the second field corresponds to a different database (GSE) sample set with the first field, the second field is a gene sequence list field, and the second field comprises at least one index of a gene sequence;
querying the second field within the second input box in response to the second query request;
sensing a query request on the second field triggering an index of a first genetic sequence belonging to one of the indexes of the at least one genetic sequence;
And responding to the query request triggering the index of the first gene sequence, querying a gene signal path of the first gene sequence in the second input frame, and updating the first field queried in the first input frame into the second field, wherein the gene signal path of the first gene sequence corresponds to a different GSE sample set with the second field.
Further, after querying a genetic signal pathway of the first genetic sequence within the second input box and updating the first field queried within the first input box to the second field, the risk prediction system further performs:
sensing a query request on the second field triggering an index of a second genetic sequence belonging to one of the indexes of the at least one genetic sequence;
responding to the query request triggering the index of a second gene sequence, querying a gene signal path of the second gene sequence in the second input frame, and still querying the second field in the first input frame, wherein the gene signal path of the second gene sequence corresponds to a different GSE sample set with the second field;
The difference query request is a query request for opening the first prediction condition;
the first input frame is positioned above the second input frame;
the number of the query immune infiltration algorithm of the first field when the first input box is queried and the number of the query immune infiltration algorithm of the second field when the second input box is queried are multiple.
Further, the second field is a list field queried by a user after searching the first field for the gene sequence;
the risk prediction system stores a query scheme corresponding to the first prediction condition; when the risk prediction system runs the first prediction condition, inquiring each field of the first prediction condition according to an inquiry scheme corresponding to the first prediction condition;
the querying, in response to the differential query request, a first field of a first prediction condition within a first input box of a query form of the risk prediction system, comprising:
in response to the differential query request, querying the first field of the first prediction condition within the first input box of a query form of the risk prediction system in a heat map query state;
the second field has a lower level than the first field;
The level of the field corresponding to the gene signal path of the first gene sequence is lower than the level of the second field;
when a first field of a first prediction condition is queried within a first input box of a query form of the risk prediction system in response to the differential query request, a field of the first prediction condition is not queried within the second input box.
Further, the absolute value of the difference between the thermodynamic diagram coordinate scale of each input box and the preset proportion is smaller than or equal to a first preset value, and the thermodynamic diagram coordinate scale is the ratio of the ordinate to the abscissa of the input box in the state of thermodynamic diagram inquiry;
when the risk prediction system queries the second field in the second input box in response to the second query request, the first field is still queried in the first input box;
the heat map query state is: the degree of correlation between the immune cell abundance field of the query form and the gene expression quantity and the signal path score is smaller than or equal to a second preset value;
the risk prediction system further performs, prior to sensing the differential query request:
inquiring the desktop in a heat map inquiring state;
the difference query request is a query request for opening the first prediction condition;
The risk prediction system further performs, prior to sensing the differential query request:
querying the first field in a relevance query state;
the difference query request is a query request that switches from the dependency query state to a heat map query state.
Further, the querying the first field of the first prediction condition in the first input box of the query form of the risk prediction system includes:
if the first prediction condition does not support heat map query, querying a first field of the first prediction condition in a first input frame of the query form;
if the first prediction condition supports heat map query, prompting a user to select a query mode, wherein the query mode comprises a heat map query mode and an input box query mode; responding to the user selection of the input box query mode, and querying a first field of the first prediction condition in a first input box of the query form;
the risk prediction system also performs the following method:
if the risk prediction system is switched to a correlation query state, querying a field of the first prediction condition in the correlation query state;
wherein, the correlation inquiry state is: the degree of correlation between the immune cell abundance field and the gene expression quantity and the signal path score of the query form is greater than a second preset value;
The field of the first prediction condition of the query is the field of the latest query of the risk prediction system in a heat map query state; or the field of the first prediction condition of the query is a field in which the risk prediction system senses the user query request at the latest in a heat map query state;
or the field of the first prediction condition of the query is the field of the latest query in a preset input frame of the risk prediction system in a heat map query state;
querying a first field of a first prediction condition within a first input box of a lookup table of the risk prediction system includes:
querying the first field of the first prediction condition in the first input frame after querying the tumor associated data of the first prediction condition in the first input frame;
the risk prediction system also performs the following method:
upon sensing a query request on a first field of the first predicted condition indicating to open a second predicted condition, querying the field of the second predicted condition within the second input box; the cancer element character string queried in the first input frame is unchanged;
the difference query request is a gesture query request or a voice query request of a user;
The second query request is a trigger query request on the first field, and the risk prediction system further performs the following method:
responding to the second query request, wherein the query effect of the triggered cancer element character string on the first field is changed;
one or more of the data source, length, immune infiltration algorithm, pattern type, or query form of the triggered cancer element string changes.
The invention discloses a virus cancer risk prediction method and a virus cancer risk prediction system based on a time sequence change relation. The latest and most comprehensive analysis of tumor immunity infiltration and various immunity infiltration algorithms and visual presentation of virus positive groups and virus negative groups in cancer patients can be carried out by a user through simple clicking, so that the further development of the tumor virology direction immunity treatment field is greatly promoted, and the value of the current data resources is released.
Drawings
FIG. 1 is a workflow diagram of a method for predicting viral cancer risk based on time series variation relationships as claimed in the present invention;
FIG. 2 is a first visual interface diagram of a method for predicting viral cancer risk based on time series variation relationships according to the present invention;
FIG. 3 is a second workflow diagram of a method for predicting viral cancer risk based on time series variation relationships as claimed in the present invention;
FIG. 4 is a third visual interface diagram of a method for predicting viral cancer risk based on time series variation relationships according to the present invention;
FIG. 5 is a block diagram of a viral cancer risk prediction system based on time series variation.
Detailed Description
According to a first embodiment of the present invention, referring to fig. 1, the present invention claims a method for predicting viral cancer risk based on time series variation relationship, the method comprising:
the risk prediction system senses a differential query request and comprises a first prediction condition, wherein the first prediction condition is a genetic immunity prediction condition;
the risk prediction system responds to the difference query request and queries a first field of a first prediction condition in a first input frame of a query form of the risk prediction system, wherein the first field is positive for virus of the first prediction condition;
The query form comprises at least two input boxes, wherein the at least two input boxes comprise a first input box and a second input box, and indexes are arranged between different input boxes;
the risk prediction system perceives a second query request on the first field, the second query request is used for opening a second field of the first prediction condition, the second field corresponds to a different database GSE sample set with the first field, the second field is a gene sequence list field, and the second field comprises at least one index of a gene sequence;
the risk prediction system queries the second field in the second input box in response to the second query request;
the risk prediction system perceives a query request on the second field triggering an index of a first genetic sequence belonging to one of the indexes of the at least one genetic sequence;
the risk prediction system responds to the query request triggering the index of the first gene sequence, queries a gene signal path of the first gene sequence in the second input frame, and updates the first field queried in the first input frame to the second field, wherein the gene signal path of the first gene sequence corresponds to a different GSE sample set with the second field.
Currently, tisideb, GEPIA2021, CAMOIP and TIP have provided many useful immunoinfiltrate-related visualizations, analysis modules and functions for tumor immunoinfiltration analysis. Although these web tools are very valuable and widely used, there is currently a lack of comprehensive molecular platforms for tumor virus-related immune microenvironment exploration. Based on the above-mentioned inadequately solved needs, we have developed a Web tool that provides comprehensive exploration of 14 tumor types, 8 virus types, 3182 tumor samples in tumor immune microenvironment, greatly promotes further development of tumor vironment-oriented immunotherapy field, thereby helping to release the value of current data resources, and provides latest and most comprehensive tumor virus-oriented immunoinfiltration analysis and various immunoinfiltration algorithms and visual presentation for clinical medicine and scientific researchers.
Prior to data query predictive input, there is a data acquisition stage in which cancer data with viral infection status is collected from a TCGA database, including TCGA-LIHC, TCGA-HNSC, TCGA-CESC and TCGA-BLCA. For example, each patient in TCGA-LIHC slit has both HBV and HCV infection status.
Each patient in TCGA-HNSC, TCGA-CESC and TCGA-BLCA probes had HPV infection status. Furthermore, we searched for and included all tumor data with viral infection status from Gene Expression Omnibus (GEO), with inclusion criteria of first, that tumor tissue samples positive for viral infection status must be greater than 10 and tumor tissue samples negative for viral infection status must also be greater than 10 (GSE 10186, GSE6791, GSE55542, GSE55544, GSE38885, GSE39612, GSE51575, GSE3292, GSE74927, GSE65858, GSE62232, GSE67763, GSE72536, GSE147704 and GSE126209; supplementary Table);
second, the tumor tissue samples positive for the viral infection state must be greater than 10 and there must be no tumor tissue samples negative for the viral infection state (GSE 173897, GSE98383, GSE94660 and GSE25599; supplementary Table 2). The expression data for each queue in the TCGA database is in both mRNA-count and mRNA-FPKM forms. The expression data of each queue in the GEO database may be RPKM, FPKM, mRNA-count or array-data.
Pretreatment is performed on the data after collection is completed, and the gene expression level is as follows: in the box plot, the mannite U test was used to compare the differences in gene expression levels in different virus infection states (expression data type is array data, FPKM and RPKM).
In the heat map, we use the gamma R package derived to compare the differences in gene expression levels under different virus infection states (expression data type is array data, FPKM and RPKM); if the expression data is RNA-count, we use edge R package to compare the differences in gene expression under different virus infection states.
For immune cells, we used five different immune infiltration assessment immune infiltration algorithms, cibelort, xCell, MCPcounter, EPIC and quanTIseq, respectively. For the pathway scores we used the ssGSEA method in the GSVA package to evaluate the pathway scores in each tumor patient. Furthermore, clusterirofiler was used for GSEA analysis and pROC was used for the plotting of ROC curves and calculation of AUC values. ggplot2, complexHeatmap and ggcorrplot is used for visualization of box plots, heat maps and correlation analysis, respectively.
The difference inquiry request plays an important role in analyzing and exploring the gene expression quantity, the immune cell abundance and the difference of the signal path fractions under different virus infection states. The user can obtain the difference of the gene expression level under different virus infection state groups. Furthermore, the user can obtain the difference between the expression amounts of all genes grouped under different virus infection states in a specific tumor. The user may use a heat or box plot format to demonstrate the differences in immune cells of interest between a particular virus positive and virus negative group.
Referring to FIG. 2, in this embodiment, the user may select the genetic markers of the query and the particular specific cancer dataset of interest.
The user can get the difference between all immune cell scores grouped under different virus infection states in a particular tumor. The user can obtain the signal path fraction difference under different virus infection states. In addition, the user may download all signal pathway score differences based on different virus infection status for a particular cancer.
Further, referring to fig. 3, after querying the genetic signal path of the first genetic sequence in the second input box and updating the first field queried in the first input box to the second field, the method further comprises:
the risk prediction system perceives a query request on the second field triggering an index of a second genetic sequence belonging to one of the indexes of the at least one genetic sequence;
the risk prediction system responds to the query request triggering the index of a second gene sequence, queries a gene signal path of the second gene sequence in the second input frame, and still queries the second field in the first input frame, wherein the gene signal path of the second gene sequence corresponds to a different GSE sample set with the second field;
The difference query request is a query request for opening the first prediction condition;
the first input frame is positioned above the second input frame;
the number of the query immune infiltration algorithm of the first field when the first input box is queried and the number of the query immune infiltration algorithm of the second field when the second input box is queried are multiple.
Based on this embodiment, the user may select a source of viral yin-yang data, and may select pearson or clearman correlation analysis algorithms; for the purpose of assessing differences between the virus positive and virus negative groups of a particular cancer patient, various immune infiltration algorithms such as differential analysis, correlation analysis, ssGSEA, CIBERSORT, xCell, MCPcounter, quanTIseq and EPIC can be used.
For example, TILs in the EBV (+) GC tumor microenvironment have significantly higher densities than other types of gastric cancer and are associated with good prognosis for EBV (+) GC patients. Some of the EBV (+) GC tumor cell surfaces mediate increased immune cell motility chemokine expression, the most representative of which are the chemokines CXCL9 and the chemokines CCL22.EBV (+) GC tumor cells express more of the chemokine CXCL9, etc.; CXCL9 can produce chemotaxis by binding to the chemokine receptor CXCR3 expressed on the surface of T cells, driving a large number of T cells to infiltrate EBV (+) GC tumor tissue. In addition, EBV infection is closely related to the regulation of PD-L1 expression on the surfaces of tumor cells such as nasopharyngeal carcinoma, hepatitis B virus-related liver cancer, human papilloma virus-positive head and neck squamous cell carcinoma and the like.
Further, the second field is a list field queried by a user after searching the first field for the gene sequence;
the risk prediction system stores a query scheme corresponding to the first prediction condition; when the risk prediction system runs the first prediction condition, inquiring each field of the first prediction condition according to an inquiry scheme corresponding to the first prediction condition;
the risk prediction system, in response to the differential query request, queries a first field of a first prediction condition within a first input box of a query form of the risk prediction system, including:
the risk prediction system responds to the difference query request and queries the first field of the first prediction condition in the first input box of a query form of the risk prediction system in a heat map query state;
the second field has a lower level than the first field;
the level of the field corresponding to the gene signal pathway of the first gene sequence is lower than the level of the second field.
Further, when the risk prediction system queries a first field of a first prediction condition in a first input box of a query form of the risk prediction system in response to the differential query request, not querying a field of the first prediction condition in the second input box;
The absolute value of the difference value between the thermodynamic diagram coordinate scale of each input frame and the preset proportion is smaller than or equal to a first preset value, and the thermodynamic diagram coordinate scale is the ratio of the ordinate to the abscissa of the input frame in the thermodynamic diagram inquiry state;
when the risk prediction system queries the second field in the second input box in response to the second query request, the first field is still queried in the first input box;
the heat map query state is: the degree of correlation between the immune cell abundance field of the query form and the gene expression quantity and the signal path score is smaller than or equal to a second preset value;
before the risk prediction system perceives the differential query request, the method further comprises:
the risk prediction system queries a desktop in a heat map query state;
the difference query request is a query request for opening the first prediction condition;
before the risk prediction system perceives the differential query request, the method further comprises:
the risk prediction system queries the first field in a correlation query state;
the difference query request is a query request switched from the correlation query state to a heat map query state;
The risk prediction system queries a first field of a first prediction condition within a first input box of a lookup table of the risk prediction system, comprising:
if the first prediction condition does not support heat map query, the risk prediction system queries a first field of the first prediction condition in a first input box of the query form;
if the first prediction condition supports heat map query, the risk prediction system prompts a user to select a query mode, wherein the query mode comprises a heat map query mode and an input box query mode; in response to a user selecting the input box query, the risk prediction system queries a first field of the first prediction condition within a first input box of the query form.
The correlation analysis in the heat map query state can be used to explore the degree of correlation between gene expression levels, immune cell abundance, and signal pathway scores in virus positive or negative groups. The user can analyze the correlation between immune cell scores, or between immune scores and gene expression levels, or between immune scores and pathway scores at different virus infection states in a particular cancer species. To explore the correlation between an immune cell score, or immune score and gene expression, or immune score and pathway score, in greater numbers, we used a correlation heat map to further evaluate. The R values for different viral infection states for a particular cancer class are shown on the right side of the heat map. Referring to FIG. 4, a correlation score under the pearson algorithm between immune cell T cells and the biocarta card er signaling pathway is shown in this example.
The R value refers to a basic index of viral infectivity, also known as the basic regeneration number. This index is used to measure how many others a person infected with a virus will on average transmit, thereby assessing the speed and extent of disease transmission in the population. The R value is calculated by observing the historical data of virus transmission and counting how many other people are infected by each infected person on average. If the R value is greater than 1, the disease will spread rapidly, and if it is less than 1, the disease will gradually resolve. In addition to the R value, other indicators may be used to measure the infectivity of the disease, such as the rate and period of infection. The R value is one of the most commonly used indexes because it can rapidly reflect the condition of disease transmission in a short time and can be used to predict future transmission trend.
Further, the method further comprises:
if the risk prediction system is switched to a correlation query state, the risk prediction system queries a field of the first prediction condition in the correlation query state;
wherein, the correlation inquiry state is: the degree of correlation between the immune cell abundance field and the gene expression quantity and the signal path score of the query form is greater than a second preset value;
The field of the first prediction condition of the query is the field of the latest query of the risk prediction system in a heat map query state;
or, the field of the first prediction condition of the query is a field in which the risk prediction system senses the user query request at the latest in a heat map query state;
or, the field of the first prediction condition of the query is the field of the latest query in a preset input frame of the risk prediction system in the heat map query state;
querying a first field of a first prediction condition within a first input box of a lookup table of the risk prediction system includes:
querying the first field of the first prediction condition in the first input frame after querying the tumor associated data of the first prediction condition in the first input frame;
the risk prediction system perceives a query request on a first field of the first prediction condition for indicating to open a second prediction condition, and queries a field of the second prediction condition in the second input box; the cancer element character string queried in the first input frame is unchanged;
the difference query request is a gesture query request or a voice query request of a user;
The second query request is a trigger query request on the first field, the method further comprising:
responding to the second query request, wherein the query effect of the triggered cancer element character string on the first field is changed;
one or more of the data source, length, immune infiltration algorithm, pattern type, or query form of the triggered cancer element string changes.
In this embodiment, GSEA may be used to explore the differences between the pathway enrichment scores in GO-BP, GO-CC, GO-MF, KEGG and reactiome databases for virus positive and virus negative data sources.
The user can select different visualization forms (such as GSEA-Plot, bar-Plot, dot-Plot, map-Plot, ridge-Plot, pathview) to display the result obtained after GSEA analysis. In addition, the user may download results regarding GSEA under different virus infection groupings for a particular cancer type; in this table, the user can obtain signaling pathways that are up-or down-regulated with respect to the virus-positive or virus-negative group in a particular tumor.
According to a second embodiment of the present invention, referring to fig. 5, the present invention claims a viral cancer risk prediction system based on a time series variation relationship, comprising a lookup table, a memory, one or more processors, a first prediction condition, and one or more programs; wherein the one or more programs are stored in the memory; the one or more processors, when executing the one or more programs, cause the risk prediction system to perform the method of:
Sensing a difference query request;
responding to the difference query request, and querying a first field of the first prediction condition in a first input frame of a query form of the risk prediction system, wherein the first field is virus negative positive of the first prediction condition, and the first prediction condition is a genetic immunity prediction condition; the query form comprises at least two input boxes, wherein the at least two input boxes comprise a first input box and a second input box, and indexes are arranged between different input boxes;
sensing a second query request on the first field, wherein the second query request is used for opening a second field of the first prediction condition, the second field corresponds to a different database (GSE) sample set with the first field, the second field is a gene sequence list field, and the second field comprises at least one index of a gene sequence;
querying the second field within the second input box in response to the second query request;
sensing a query request on the second field triggering an index of a first genetic sequence belonging to one of the indexes of the at least one genetic sequence;
And responding to the query request triggering the index of the first gene sequence, querying a gene signal path of the first gene sequence in the second input frame, and updating the first field queried in the first input frame into the second field, wherein the gene signal path of the first gene sequence corresponds to a different GSE sample set with the second field.
Further, after querying a genetic signal pathway of the first genetic sequence within the second input box and updating the first field queried within the first input box to the second field, the risk prediction system further performs:
sensing a query request on the second field triggering an index of a second genetic sequence belonging to one of the indexes of the at least one genetic sequence;
responding to the query request triggering the index of a second gene sequence, querying a gene signal path of the second gene sequence in the second input frame, and still querying the second field in the first input frame, wherein the gene signal path of the second gene sequence corresponds to a different GSE sample set with the second field;
The difference query request is a query request for opening the first prediction condition;
the first input frame is positioned above the second input frame;
the number of the query immune infiltration algorithm of the first field when the first input box is queried and the number of the query immune infiltration algorithm of the second field when the second input box is queried are multiple.
Further, the second field is a list field queried by a user after searching the first field for the gene sequence;
the risk prediction system stores a query scheme corresponding to the first prediction condition; when the risk prediction system runs the first prediction condition, inquiring each field of the first prediction condition according to an inquiry scheme corresponding to the first prediction condition;
the querying, in response to the differential query request, a first field of a first prediction condition within a first input box of a query form of the risk prediction system, comprising:
in response to the differential query request, querying the first field of the first prediction condition within the first input box of a query form of the risk prediction system in a heat map query state;
the second field has a lower level than the first field;
The level of the field corresponding to the gene signal path of the first gene sequence is lower than the level of the second field;
when a first field of a first prediction condition is queried within a first input box of a query form of the risk prediction system in response to the differential query request, a field of the first prediction condition is not queried within the second input box.
Further, the absolute value of the difference between the thermodynamic diagram coordinate scale of each input box and the preset proportion is smaller than or equal to a first preset value, and the thermodynamic diagram coordinate scale is the ratio of the ordinate to the abscissa of the input box in the state of thermodynamic diagram inquiry;
when the risk prediction system queries the second field in the second input box in response to the second query request, the first field is still queried in the first input box;
the heat map query state is: the degree of correlation between the immune cell abundance field of the query form and the gene expression quantity and the signal path score is smaller than or equal to a second preset value;
the risk prediction system further performs, prior to sensing the differential query request:
inquiring the desktop in a heat map inquiring state;
the difference query request is a query request for opening the first prediction condition;
The risk prediction system further performs, prior to sensing the differential query request:
querying the first field in a relevance query state;
the difference query request is a query request that switches from the dependency query state to a heat map query state.
Further, the querying the first field of the first prediction condition in the first input box of the query form of the risk prediction system includes:
if the first prediction condition does not support heat map query, querying a first field of the first prediction condition in a first input frame of the query form;
if the first prediction condition supports heat map query, prompting a user to select a query mode, wherein the query mode comprises a heat map query mode and an input box query mode; responding to the user selection of the input box query mode, and querying a first field of the first prediction condition in a first input box of the query form;
the risk prediction system also performs the following method:
if the risk prediction system is switched to a correlation query state, querying a field of the first prediction condition in the correlation query state;
wherein, the correlation inquiry state is: the degree of correlation between the immune cell abundance field and the gene expression quantity and the signal path score of the query form is greater than a second preset value;
The field of the first prediction condition of the query is the field of the latest query of the risk prediction system in a heat map query state; or the field of the first prediction condition of the query is a field in which the risk prediction system senses the user query request at the latest in a heat map query state;
or the field of the first prediction condition of the query is the field of the latest query in a preset input frame of the risk prediction system in a heat map query state;
querying a first field of a first prediction condition within a first input box of a lookup table of the risk prediction system includes:
querying the first field of the first prediction condition in the first input frame after querying the tumor associated data of the first prediction condition in the first input frame;
the risk prediction system also performs the following method:
upon sensing a query request on a first field of the first predicted condition indicating to open a second predicted condition, querying the field of the second predicted condition within the second input box; the cancer element character string queried in the first input frame is unchanged;
the difference query request is a gesture query request or a voice query request of a user;
The second query request is a trigger query request on the first field, and the risk prediction system further performs the following method:
responding to the second query request, wherein the query effect of the triggered cancer element character string on the first field is changed;
one or more of the data source, length, immune infiltration algorithm, pattern type, or query form of the triggered cancer element string changes.
Those skilled in the art will appreciate that many variations and modifications may be made to the cancer element strings disclosed in the present disclosure. For example, the various devices or components described above may be implemented in hardware, or may be implemented in software, firmware, or a combination of some or all of the three.
A flowchart is used in this disclosure to describe the steps of a method according to an embodiment of the present disclosure. It should be understood that the steps that follow or before do not have to be performed in exact order. Rather, the various steps may be processed in reverse order or simultaneously. At the same time, other query requests may be added to these processes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of the methods described above may be implemented by a computer program to instruct related hardware, and the program may be stored in a computer readable storage medium, such as a read only memory, a magnetic disk, or an optical disk. Alternatively, all or part of the steps of the above embodiments may be implemented using one or more integrated circuits. Accordingly, each module/unit in the above embodiment may be implemented in the form of hardware, or may be implemented in the form of a software functional module. The present disclosure is not limited to any specific form of combination of hardware and software.
Unless defined otherwise, all terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The foregoing is illustrative of the present disclosure and is not to be construed as limiting thereof. Although a few exemplary embodiments of this disclosure have been described, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this disclosure. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the claims. It is to be understood that the foregoing is illustrative of the present disclosure and is not to be construed as limited to the specific embodiments disclosed, and that modifications to the disclosed embodiments, as well as other embodiments, are intended to be included within the scope of the appended claims. The disclosure is defined by the claims and their equivalents.
In the description of the present specification, 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 invention. 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.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (8)
1. A method for predicting viral cancer risk based on a time series variation relationship, the method comprising:
the risk prediction system senses a differential query request and comprises a first prediction condition, wherein the first prediction condition is a genetic immunity prediction condition;
The risk prediction system responds to the difference query request and queries a first field of a first prediction condition in a first input frame of a query form of the risk prediction system, wherein the first field is positive for virus of the first prediction condition;
the query form comprises at least two input boxes, wherein the at least two input boxes comprise a first input box and a second input box, and indexes are arranged between different input boxes;
the risk prediction system perceives a second query request on the first field, the second query request is used for opening a second field of the first prediction condition, the second field corresponds to a different database GSE sample set with the first field, the second field is a gene sequence list field, and the second field comprises at least one index of a gene sequence;
the risk prediction system queries the second field in the second input box in response to the second query request;
the risk prediction system perceives a query request on the second field triggering an index of a first genetic sequence belonging to one of the indexes of the at least one genetic sequence;
The risk prediction system responds to the query request triggering the index of a first gene sequence, queries a gene signal path of the first gene sequence in the second input frame, and updates the first field queried in the first input frame to the second field, wherein the gene signal path of the first gene sequence corresponds to a different GSE sample set with the second field;
after querying a gene signal pathway of the first gene sequence within the second input box and updating the first field of the first input box query to the second field, the method further comprises:
the risk prediction system perceives a query request on the second field triggering an index of a second genetic sequence belonging to one of the indexes of the at least one genetic sequence;
the risk prediction system responds to the query request triggering the index of a second gene sequence, queries a gene signal path of the second gene sequence in the second input frame, and still queries the second field in the first input frame, wherein the gene signal path of the second gene sequence corresponds to a different GSE sample set with the second field;
The difference query request is a query request for opening the first prediction condition;
the first input frame is positioned above the second input frame;
the number of the query immune infiltration algorithm of the first field when the first input box is queried and the number of the query immune infiltration algorithm of the second field when the second input box is queried are multiple.
2. The method of claim 1, wherein the second field is a list field queried by a user after searching for a gene sequence on the first field;
the risk prediction system stores a query scheme corresponding to the first prediction condition; when the risk prediction system runs the first prediction condition, inquiring each field of the first prediction condition according to an inquiry scheme corresponding to the first prediction condition;
the risk prediction system, in response to the differential query request, queries a first field of a first prediction condition within a first input box of a query form of the risk prediction system, including:
the risk prediction system responds to the difference query request and queries the first field of the first prediction condition in the first input box of a query form of the risk prediction system in a heat map query state;
The second field has a lower level than the first field;
the level of the field corresponding to the gene signal pathway of the first gene sequence is lower than the level of the second field.
3. The method of claim 2, wherein when the risk prediction system queries a first field of a first prediction condition in a first input box of a query form of the risk prediction system in response to the differential query request, the second input box does not query a field of the first prediction condition;
the absolute value of the difference value between the thermodynamic diagram coordinate scale of each input frame and the preset proportion is smaller than or equal to a first preset value, and the thermodynamic diagram coordinate scale is the ratio of the ordinate to the abscissa of the input frame in the thermodynamic diagram inquiry state;
when the risk prediction system queries the second field in the second input box in response to the second query request, the first field is still queried in the first input box;
the heat map query state is: the degree of correlation between the immune cell abundance field of the query form and the gene expression quantity and the signal path score is smaller than or equal to a second preset value;
Before the risk prediction system perceives the differential query request, the method further comprises:
the risk prediction system queries a desktop in a heat map query state;
the difference query request is a query request for opening the first prediction condition;
before the risk prediction system perceives the differential query request, the method further comprises:
the risk prediction system queries the first field in a correlation query state;
the difference query request is a query request switched from the correlation query state to a heat map query state;
the risk prediction system queries a first field of a first prediction condition within a first input box of a lookup table of the risk prediction system, comprising:
if the first prediction condition does not support heat map query, the risk prediction system queries a first field of the first prediction condition in a first input box of the query form;
if the first prediction condition supports heat map query, the risk prediction system prompts a user to select a query mode, wherein the query mode comprises a heat map query mode and an input box query mode; in response to a user selecting the input box query, the risk prediction system queries a first field of the first prediction condition within a first input box of the query form.
4. A method according to claim 3, characterized in that the method further comprises:
if the risk prediction system is switched to a correlation query state, the risk prediction system queries a field of the first prediction condition in the correlation query state;
wherein, the correlation inquiry state is: the degree of correlation between the immune cell abundance field and the gene expression quantity and the signal path score of the query form is greater than a second preset value;
the field of the first prediction condition of the query is the field of the latest query of the risk prediction system in a heat map query state;
or, the field of the first prediction condition of the query is a field in which the risk prediction system senses the user query request at the latest in a heat map query state;
or, the field of the first prediction condition of the query is the field of the latest query in a preset input frame of the risk prediction system in the heat map query state;
querying a first field of a first prediction condition within a first input box of a lookup table of the risk prediction system includes:
querying the first field of the first prediction condition in the first input frame after querying the tumor associated data of the first prediction condition in the first input frame;
The risk prediction system perceives a query request on a first field of the first prediction condition for indicating to open a second prediction condition, and queries a field of the second prediction condition in the second input box; the cancer element character string queried in the first input frame is unchanged;
the difference query request is a gesture query request or a voice query request of a user;
the second query request is a trigger query request on the first field, the method further comprising:
responding to the second query request, wherein the query effect of the triggered cancer element character string on the first field is changed;
one or more of the data source, length, immune infiltration algorithm, pattern type, or query form of the triggered cancer element string changes.
5. A viral cancer risk prediction system based on a time series variation relationship, comprising a lookup table, a memory, one or more processors, a first prediction condition, and one or more programs; wherein the one or more programs are stored in the memory; wherein the one or more processors, when executing the one or more programs, cause the risk prediction system to perform the method of:
Sensing a difference query request;
responding to the difference query request, and querying a first field of the first prediction condition in a first input frame of a query form of the risk prediction system, wherein the first field is virus negative positive of the first prediction condition, and the first prediction condition is a genetic immunity prediction condition; the query form comprises at least two input boxes, wherein the at least two input boxes comprise a first input box and a second input box, and indexes are arranged between different input boxes;
sensing a second query request on the first field, wherein the second query request is used for opening a second field of the first prediction condition, the second field corresponds to a different database (GSE) sample set with the first field, the second field is a gene sequence list field, and the second field comprises at least one index of a gene sequence;
querying the second field within the second input box in response to the second query request;
sensing a query request on the second field triggering an index of a first genetic sequence belonging to one of the indexes of the at least one genetic sequence;
Responding to the query request triggering the index of the first gene sequence, querying a gene signal path of the first gene sequence in the second input frame, and updating the first field queried in the first input frame into the second field, wherein the gene signal path of the first gene sequence corresponds to a different GSE sample set with the second field;
after querying a genetic signal pathway of the first genetic sequence in the second input box and updating the first field of the first input box query to the second field, the risk prediction system further performs:
sensing a query request on the second field triggering an index of a second genetic sequence belonging to one of the indexes of the at least one genetic sequence;
responding to the query request triggering the index of a second gene sequence, querying a gene signal path of the second gene sequence in the second input frame, and still querying the second field in the first input frame, wherein the gene signal path of the second gene sequence corresponds to a different GSE sample set with the second field;
the difference query request is a query request for opening the first prediction condition;
The first input frame is positioned above the second input frame;
the number of the query immune infiltration algorithm of the first field when the first input box is queried and the number of the query immune infiltration algorithm of the second field when the second input box is queried are multiple.
6. The viral cancer risk prediction system based on time series variation relationships according to claim 5, wherein the second field is a list field queried by a user after searching for a gene sequence on the first field;
the risk prediction system stores a query scheme corresponding to the first prediction condition; when the risk prediction system runs the first prediction condition, inquiring each field of the first prediction condition according to an inquiry scheme corresponding to the first prediction condition;
the querying, in response to the differential query request, a first field of a first prediction condition within a first input box of a query form of the risk prediction system, comprising:
in response to the differential query request, querying the first field of the first prediction condition within the first input box of a query form of the risk prediction system in a heat map query state;
The second field has a lower level than the first field;
the level of the field corresponding to the gene signal path of the first gene sequence is lower than the level of the second field;
when a first field of a first prediction condition is queried within a first input box of a query form of the risk prediction system in response to the differential query request, a field of the first prediction condition is not queried within the second input box.
7. The viral cancer risk prediction system based on time series variation relationships according to claim 6, wherein an absolute value of a difference between a thermodynamic diagram coordinate scale of each input frame and a preset ratio is less than or equal to a first preset value, the thermodynamic diagram coordinate scale being a ratio of an ordinate of the input frame to an abscissa of the input frame in a thermodynamic diagram query state;
when the risk prediction system queries the second field in the second input box in response to the second query request, the first field is still queried in the first input box;
the heat map query state is: the degree of correlation between the immune cell abundance field of the query form and the gene expression quantity and the signal path score is smaller than or equal to a second preset value;
The risk prediction system further performs, prior to sensing the differential query request:
inquiring the desktop in a heat map inquiring state;
the difference query request is a query request for opening the first prediction condition;
the risk prediction system further performs, prior to sensing the differential query request:
querying the first field in a relevance query state;
the difference query request is a query request that switches from the dependency query state to a heat map query state.
8. The viral cancer risk prediction system based on time series variation relationships according to claim 7, wherein said querying a first field of a first prediction condition within a first input box of a lookup table of the risk prediction system comprises:
if the first prediction condition does not support heat map query, querying a first field of the first prediction condition in a first input frame of the query form;
if the first prediction condition supports heat map query, prompting a user to select a query mode, wherein the query mode comprises a heat map query mode and an input box query mode; responding to the user selection of the input box query mode, and querying a first field of the first prediction condition in a first input box of the query form;
The risk prediction system also performs the following method:
if the risk prediction system is switched to a correlation query state, querying a field of the first prediction condition in the correlation query state;
wherein, the correlation inquiry state is: the degree of correlation between the immune cell abundance field and the gene expression quantity and the signal path score of the query form is greater than a second preset value;
the field of the first prediction condition of the query is the field of the latest query of the risk prediction system in a heat map query state; or alternatively
The field of the first prediction condition of the query is a field in which the risk prediction system senses a user query request at the latest in a heat map query state; or alternatively
The field of the first prediction condition of the query is the field of the latest query in a preset input frame of the risk prediction system in a heat map query state;
querying a first field of a first prediction condition within a first input box of a lookup table of the risk prediction system includes:
querying the first field of the first prediction condition in the first input frame after querying the tumor associated data of the first prediction condition in the first input frame;
The risk prediction system also performs the following method:
upon sensing a query request on a first field of the first predicted condition indicating to open a second predicted condition, querying the field of the second predicted condition within the second input box; the cancer element character string queried in the first input frame is unchanged;
the difference query request is a gesture query request or a voice query request of a user;
the second query request is a trigger query request on the first field, and the risk prediction system further performs the following method:
responding to the second query request, wherein the query effect of the triggered cancer element character string on the first field is changed;
one or more of the data source, length, immune infiltration algorithm, pattern type, or query form of the triggered cancer element string changes.
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