CN115881228B - Gene detection data cleaning method and system based on artificial intelligence - Google Patents
Gene detection data cleaning method and system based on artificial intelligence Download PDFInfo
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Abstract
The invention provides a gene detection data cleaning method and system based on artificial intelligence; wherein the method comprises the following steps: performing first processing on the gene detection data to obtain first abnormal data; the anomaly identification model is built based on an artificial intelligence algorithm; acquiring link data of the gene detection data, and determining an anomaly identification model according to the link data; identifying the first abnormal data by using the abnormal identification model so as to obtain second abnormal data; and cleaning the second abnormal data. According to the scheme, on one hand, the abnormal data can be identified and cleaned, and on the other hand, the efficiency of identifying the abnormal data of the gene detection can be remarkably improved by adopting a step-by-step identification and personalized identification processing mode.
Description
Technical Field
The invention relates to the technical field of gene detection, in particular to a gene detection data cleaning method, system, electronic equipment and computer storage medium based on artificial intelligence.
Background
The gene is a basic unit of inheritance, and a DNA or RNA sequence carrying genetic information is replicated to transfer the genetic information to the next generation, so that the synthesis of proteins is guided to express the genetic information carried by the gene, and the expression of the characteristics of an individual organism is controlled.
The gene detection is a technology for detecting DNA through blood, other body fluids or cells, and is a method for detecting DNA molecular information in the cells of a detected person through specific equipment after the peripheral venous blood or other tissue cells of the detected person are taken and the gene information is amplified, so that people can know own gene information, and the cause of disease is clear or the risk of the body suffering from a certain disease is predicted. Gene detection can also be used to analyze other physical conditions, such as by analyzing gene detection data to obtain parameters such as sensitivity, light resistance, sugar resistance, wrinkle resistance, oxidation resistance, etc. of the skin.
In practical experience, it has been found that various factors in the genetic testing procedure can cause abnormal data such as probe failure, equipment failure, contamination of gene samples with interference sources, etc. in the genetic testing data obtained by the initial test, and the abnormal data needs to be cleaned before genetic analysis is performed, which would affect the accuracy of the genetic analysis result. However, the prior art does not solve the technical problem well.
Disclosure of Invention
In order to at least solve the technical problems in the background art, the invention provides a gene detection data cleaning method, a system, electronic equipment and a computer storage medium based on artificial intelligence.
The first aspect of the invention provides a gene detection data cleaning method based on artificial intelligence, comprising the following steps:
performing first processing on the gene detection data to obtain first abnormal data;
acquiring link data of the gene detection data, and determining an anomaly identification model according to the link data; the anomaly identification model is built based on an artificial intelligence algorithm;
identifying the first abnormal data by using the abnormal identification model so as to obtain second abnormal data;
and cleaning the second abnormal data.
Further, the first processing of the gene detection data to obtain first abnormal data includes:
comparing each sub-segment data of the gene detection data with the corresponding identification sub-rule in the abnormal identification rule set;
and determining a plurality of sub-segment data as the first abnormal data according to the comparison result.
Further, the obtaining the link data of the gene detection data includes:
determining the data of the nodes of the gene detection data, and acquiring auxiliary data associated with the data of the nodes of the gene detection data;
and taking the auxiliary data as the link data.
Further, the acquiring assistance data associated with the already-traversed node data includes:
determining first attribute data corresponding to each of the nodes, and determining an acquisition range according to the first attribute data;
and acquiring auxiliary data according to the acquisition range.
Further, the determining an anomaly identification model according to the link data includes:
evaluating the auxiliary data corresponding to each of the experienced nodes to obtain a first evaluation value;
determining a weight coefficient according to the second attribute data corresponding to each of the traversed nodes;
fusing the first evaluation values of all the already-traversed nodes according to the weight coefficients to obtain second evaluation values;
and determining the abnormal recognition model according to the second evaluation value and a preset relation.
Further, the determining the anomaly identification model according to the second evaluation value and a preset relationship includes:
if the second evaluation value is located in the first interval, an artificial abnormality recognition model is selected as the abnormality recognition model;
and if the second evaluation value is located in the second interval, selecting an equipment abnormality recognition model as the abnormality recognition model.
Further, the cleaning the second abnormal data includes:
and deleting and/or replacing the second abnormal data.
The invention provides a gene detection data cleaning system based on artificial intelligence, which comprises a receiving module, a processing module and a storage module; the processing module is connected with the receiving module and the storage module;
the memory module is used for storing executable computer program codes;
the receiving module is used for receiving the gene detection data and related data thereof and transmitting the gene detection data and the related data to the processing module;
the processing module is configured to perform the method of any of the preceding claims by invoking the executable computer program code in the storage module.
A third aspect of the present invention provides an electronic device comprising: a memory storing executable program code; a processor coupled to the memory; the processor invokes the executable program code stored in the memory to perform the method of any one of the preceding claims.
A fourth aspect of the invention provides a computer storage medium having stored thereon a computer program which, when executed by a processor, performs a method as claimed in any one of the preceding claims.
According to the scheme, the first abnormal data which possibly has abnormality is weakly identified through the preset rule, and the proper abnormality identification model is determined according to the link data of the gene detection data, so that the first abnormal data is subjected to targeted strong identification, and the real abnormal data can be locked and cleaned. Therefore, the scheme of the invention can realize the identification and cleaning of the abnormal data on one hand, and can obviously improve the efficiency of identifying the abnormal data of the gene detection by adopting a processing mode of step-by-step identification and personalized identification on the other hand.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a gene detection data cleaning method based on artificial intelligence according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a system for cleaning gene detection data based on artificial intelligence according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the examples and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, the "plurality" generally includes at least two.
It should be understood that the term "and/or" as used herein is merely one relationship describing the association of the associated objects, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
It should be understood that although the terms first, second, third, etc. may be used in embodiments of the present application to describe … …, these … … should not be limited to these terms. These terms are only used to distinguish … …. For example, the first … … may also be referred to as the second … …, and similarly the second … … may also be referred to as the first … …, without departing from the scope of embodiments of the present application.
The words "if", as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a product or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such product or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a commodity or system comprising such elements.
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for cleaning gene detection data based on artificial intelligence according to an embodiment of the invention. As shown in FIG. 1, the gene detection data cleaning method based on artificial intelligence in the embodiment of the invention comprises the following steps:
performing first processing on the gene detection data to obtain first abnormal data;
acquiring link data of the gene detection data, and determining an anomaly identification model according to the link data; the anomaly identification model is built based on an artificial intelligence algorithm;
identifying the first abnormal data by using the abnormal identification model so as to obtain second abnormal data;
and cleaning the second abnormal data.
In the embodiment of the invention, the first abnormal data which possibly has abnormality is weakly identified through the preset rule, and then the proper abnormality identification model is determined according to the link data of the gene detection data, so that the first abnormal data is subjected to targeted strong identification, and the real abnormal data can be locked and cleaned. Therefore, the scheme of the invention can realize the identification and cleaning of the abnormal data on one hand, and can obviously improve the efficiency of identifying the abnormal data of the gene detection by adopting a processing mode of step-by-step identification and personalized identification on the other hand.
The execution subject of this and subsequent modified embodiments of the present invention may be a field processing device deployed in a detection analysis center, or may be a server located at a remote location. The field processing devices include, but are not limited to, central processing units, general purpose processors, digital signal processors, application specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.; and the server can be a single server or a cloud server cluster formed by a plurality of servers, such as an ali cloud, a messenger cloud and the like. The field processing equipment and the server communicate through a network to realize the transmission of the gene detection data. The network can include, by way of non-limiting example: a temporary network (ad hoc network), an intranet, an extranet, a virtual private network (virtual private network: VPN), a local area network (local area network: LAN), a wireless LAN (wireless LAN: WLAN), a wide area network (wide area network: WAN), a wireless WAN (wireless WAN: WWAN), a metropolitan area network (metropolitan area network: MAN), a portion of the Internet, a portion of the public switched telephone network (Public Switched Telephone Network: PSTN), a mobile telephone network, an ISDN (integrated service digital networks, an integrated services digital network), a wireless LAN, LTE (long term evolution ), CDMA (code division multiple access, code division multiple access), bluetooth (Bluetooth), satellite communications, and the like, or a combination of two or more of these.
Further, the first processing of the gene detection data to obtain first abnormal data includes:
comparing each sub-segment data of the gene detection data with the corresponding identification sub-rule in the abnormal identification rule set;
and determining a plurality of sub-segment data as the first abnormal data according to the comparison result.
In the embodiment of the invention, an abnormality recognition rule set is constructed in advance based on basic rule features (normal and standard gene data) of human gene data, and then each sub-segment data of gene detection data is evaluated by using the abnormality recognition rule set, so that first abnormal data of potential abnormality can be rapidly determined and sent to a subsequent step for strong recognition.
Further, the obtaining the link data of the gene detection data includes:
determining the data of the nodes of the gene detection data, and acquiring auxiliary data associated with the data of the nodes of the gene detection data;
and taking the auxiliary data as the link data.
In the embodiment of the present invention, the gene detection data/gene samples in the gene detection flow may involve a plurality of different nodes, for example, the already-traversed node data of a certain gene detection data a includes a detection node (fixed or temporary detection window), a transit node (transfer, transmission, etc.), a processing node (such as a gene sample preprocessing link, a gene data extraction link, etc.), and each link has a different probability of causing abnormality of the final gene detection data, such as mixing blood of other animals into the gene sample, etc.
Aiming at the technical problem, the invention acquires the already-existing node data of the gene detection data, namely the node which the gene detection data has undergone before the current processing stage, further invokes auxiliary data associated with each node data, and can judge the probability of abnormality caused by human misoperation in the link according to the auxiliary data, thereby being capable of pointedly selecting an abnormality identification model for abnormality strong identification.
Further, the acquiring assistance data associated with the already-traversed node data includes:
determining first attribute data corresponding to each of the nodes, and determining an acquisition range according to the first attribute data;
and acquiring auxiliary data according to the acquisition range.
In the embodiment of the invention, each node related to the gene detection flow has different attributes, and the different attributes can reflect different probabilities of abnormality of the gene detection data caused by human errors of the node. Then, the present invention determines a first range from attribute data of each node, and then acquires auxiliary data within the first range.
Wherein the acquisition range may be a time range, for example, a [ t1, t2] period corresponding to the gene detection data; the geographical range may be, for example, site data corresponding to the gene detection data, a physical adjacency range, specifically, the geographical range may be detection sites a and B of a city (because the detection data of the two sites are intensively processed by the same upper-level site), or the geographical range may be a physically adjacent inspection room a and inspection room B in the site a, and so on, which is not described in detail.
The first attribute data of the nodes are the gene sampling items, processing efficiency, automation degree and the like of each node, and these data can reflect the probability of human errors of the corresponding nodes. For example, if a node has a large number of gene sampling items (for example, gene sampling for cosmetic purposes and animal gene sampling for academic research purposes), has low processing efficiency and automation degree, and requires more manual participation in gene sampling, and different gene samples need to be stored in the same refrigeration warehouse for a long time (long transfer period), the node has a larger acquisition range, so that more auxiliary data can be acquired, and the probability of abnormality caused by human misoperation of the node can be more accurately analyzed; for the nodes with single gene sampling items and high automation degree, the proportion of human participation is small, and the probability of human error is low, so that the related auxiliary data can be acquired only in a smaller acquisition range. The corresponding relation between various types included in the first attribute data and the acquisition range can be established in advance, and then the equivalent corresponding relation can be determined by integrating the various factors, so that the proper acquisition range can be determined quickly.
In addition, the auxiliary data may contain all or part of the same data content as the first attribute data, the main difference between the two being that the attribute data may be historical data, the auxiliary data is real-time data corresponding to the gene detection data to be cleaned, and the historical data and the real-time data may include static data and/or dynamic data of the node.
Further, the determining an anomaly identification model according to the link data includes:
evaluating the auxiliary data corresponding to each of the experienced nodes to obtain a first evaluation value;
determining a weight coefficient according to the second attribute data corresponding to each of the traversed nodes;
fusing the first evaluation values of all the already-traversed nodes according to the weight coefficients to obtain second evaluation values;
and determining the abnormal recognition model according to the second evaluation value and a preset relation.
In the embodiment of the invention, the acquired auxiliary data of each node is evaluated, and the weight coefficient is determined according to the second attribute data of each node, at the moment, the second evaluation value corresponding to the whole link data can be obtained through a method such as weighted average, and the probability of abnormality caused by manual misoperation of the link data can be determined by comparing the second evaluation value with a preset relation, so that different abnormality recognition models can be selected.
The second attribute data is different from the first attribute data, and is used for describing functional attributes of each node, including a detection node, a centralized/transit node, a communication node, a data storage node, and the like, where the probability of human errors existing in the former two nodes is greater, and the latter two nodes are only data transmission and storage nodes, and generally no influence of human errors exists, so that weight coefficients (the former two weight coefficients are large and the latter two weight coefficients are small) are correspondingly set for each node based on the characteristics. Specifically, a weight coefficient comparison table of the nodes of each attribute can be pre-established, which is not described in detail.
Further, the determining the anomaly identification model according to the second evaluation value and a preset relationship includes:
if the second evaluation value is located in the first interval, an artificial abnormality recognition model is selected as the abnormality recognition model;
and if the second evaluation value is located in the second interval, selecting an equipment abnormality recognition model as the abnormality recognition model.
In the embodiment of the invention, as described above, the reasons for the abnormality in the genetic detection data are mainly divided into two types, namely, human misoperation (such as mixing in genetic samples of other animals) and equipment faults (such as data transcription errors), so that the invention respectively builds two different abnormality recognition models according to the two conditions, builds corresponding training data sets to carry out targeted training on the two different abnormality recognition models, and enables the two models to respectively carry out human abnormality/equipment abnormality recognition judgment on the first abnormality data so as to screen out second abnormality data of real abnormality; the first interval may be larger than the second interval, that is, the abnormality caused by the human error may be determined when the second evaluation value is higher.
The artificial intelligence algorithm related in the invention can be built by adopting various algorithms such as Neural network algorithm (Neural Networks), automatic encoder (Auto Encoders), deep belief network (Deep Belief Networks), restricted Boltzmann machine (Restricted Boltzmann Machines), generation countermeasure network (Generative Adversarial Networks) and the like, and specific building and training methods are not repeated.
Further, the cleaning the second abnormal data includes:
and deleting and/or replacing the second abnormal data.
In the embodiment of the invention, after the second abnormal data with the real abnormality in the first abnormal data is determined through strong recognition, the second abnormal data can be directly deleted and replaced. For example, if the second abnormal data is a system failure (for example, a transcoding error), the method of substitution processing (differential substitution using standard data or estimated data) may be adopted, and if the second abnormal data is interference gene data of other animals, the method of direct deletion may be adopted. Of course, more processing modes can be set according to more situations which can exist actually, and the invention is not repeated.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a gene detection data cleaning system based on artificial intelligence according to an embodiment of the invention. As shown in fig. 2, the gene detection data cleaning system based on artificial intelligence in the embodiment of the invention comprises a receiving module (101), a processing module (102) and a storage module (103); the processing module (102) is connected with the receiving module (101) and the storage module (103);
-said storage module (103) for storing executable computer program code;
the receiving module (101) is used for receiving the gene detection data and the related data thereof and transmitting the gene detection data and the related data to the processing module (102);
-said processing module (102) for executing the method according to any of the preceding claims by invoking said executable computer program code in said storage module (103).
The specific function of the gene detection data cleaning system based on artificial intelligence in this embodiment refers to the above embodiment, and since the system in this embodiment adopts all the technical solutions of the above embodiment, at least the system has all the beneficial effects brought by the technical solutions of the above embodiment, and will not be described in detail herein.
Referring to fig. 3, fig. 3 is an electronic device according to an embodiment of the present invention, including: a memory storing executable program code; a processor coupled to the memory; the processor invokes the executable program code stored in the memory to perform the method as described in the previous embodiment.
The embodiment of the invention also discloses a computer storage medium, and a computer program is stored on the storage medium, and when the computer program is run by a processor, the computer program executes the method according to the previous embodiment.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the above embodiments, but may include many other equivalent embodiments without departing from the spirit of the invention, which fall within the scope of the invention.
Claims (10)
1. The gene detection data cleaning method based on artificial intelligence is characterized by comprising the following steps of:
performing first processing on the gene detection data to obtain first abnormal data;
acquiring link data of the gene detection data, and determining an anomaly identification model according to the link data; the anomaly identification model is built based on an artificial intelligence algorithm; wherein the link data is associated with a transmission node of the genetic test data;
identifying the first abnormal data by using the abnormal identification model so as to obtain second abnormal data;
and cleaning the second abnormal data.
2. The method for cleaning gene detection data based on artificial intelligence according to claim 1, wherein: the first processing of the gene detection data to obtain first abnormal data includes:
comparing each sub-segment data of the gene detection data with the corresponding identification sub-rule in the abnormal identification rule set;
and determining a plurality of sub-segment data as the first abnormal data according to the comparison result.
3. The method for cleaning gene detection data based on artificial intelligence according to claim 1, wherein: the link data for acquiring the gene detection data comprises:
determining the data of the nodes of the gene detection data, and acquiring auxiliary data associated with the data of the nodes of the gene detection data;
and taking the auxiliary data as the link data.
4. The method for cleaning gene detection data based on artificial intelligence according to claim 3, wherein: the acquiring assistance data associated with the already-traversed node data includes:
determining first attribute data corresponding to each of the nodes, and determining an acquisition range according to the first attribute data;
and acquiring auxiliary data according to the acquisition range.
5. The method for cleaning gene detection data based on artificial intelligence according to claim 4, wherein: the determining an anomaly identification model according to the link data comprises the following steps:
evaluating the auxiliary data corresponding to each of the experienced nodes to obtain a first evaluation value;
determining a weight coefficient according to the second attribute data corresponding to each of the traversed nodes;
fusing the first evaluation values of all the already-traversed nodes according to the weight coefficients to obtain second evaluation values;
and determining the abnormal recognition model according to the second evaluation value and a preset relation.
6. The method for cleaning gene detection data based on artificial intelligence according to claim 5, wherein: the determining the abnormality recognition model according to the second evaluation value and the preset relation includes:
if the second evaluation value is located in the first interval, an artificial abnormality recognition model is selected as the abnormality recognition model;
and if the second evaluation value is located in the second interval, selecting an equipment abnormality recognition model as the abnormality recognition model.
7. The method for cleaning gene detection data based on artificial intelligence according to claim 1, wherein: the cleaning the second abnormal data includes:
and deleting and/or replacing the second abnormal data.
8. The gene detection data cleaning system based on artificial intelligence comprises a receiving module, a processing module and a storage module; the processing module is connected with the receiving module and the storage module;
the memory module is used for storing executable computer program codes;
the receiving module is used for receiving the gene detection data and related data thereof and transmitting the gene detection data and the related data to the processing module;
the method is characterized in that: the processing module for performing the method of any of claims 1-7 by invoking the executable computer program code in the storage module.
9. An electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the method is characterized in that: the processor invokes the executable program code stored in the memory to perform the method of any of claims 1-7.
10. A computer storage medium having a computer program stored thereon, characterized in that: the computer program, when executed by a processor, performs the method of any of claims 1-7.
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