WO2022086053A1 - Système d'extraction de déterminant spécifique de microréseau basé sur l'intelligence artificielle - Google Patents

Système d'extraction de déterminant spécifique de microréseau basé sur l'intelligence artificielle Download PDF

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WO2022086053A1
WO2022086053A1 PCT/KR2021/014237 KR2021014237W WO2022086053A1 WO 2022086053 A1 WO2022086053 A1 WO 2022086053A1 KR 2021014237 W KR2021014237 W KR 2021014237W WO 2022086053 A1 WO2022086053 A1 WO 2022086053A1
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bacterial artificial
module
artificial chromosome
neural network
loss value
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PCT/KR2021/014237
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Korean (ko)
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이명재
강신욱
김원태
김동민
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(주)제이엘케이
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Priority claimed from KR1020210136583A external-priority patent/KR20220052279A/ko
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B45/00—ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks

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  • the present invention relates to a system for extracting chromosome probes used when classifying a class of a characteristic in a microarray.
  • a DNA microarray also known as a DNA chip, is one in which a large amount of gene fragments are attached to the surface of a glass slide in a state in which they are arranged at regular intervals.
  • Gene fragments arranged and attached at regular intervals in a DNA microarray are defined as probes, and may have a known nucleotide sequence of a specific gene.
  • DNA microarrays can be used to investigate the expression level of large amounts of genes in specific cells. For example, by examining the expression of a large amount of genes using a DNA microarray in various types of cancer cells, the similarity of gene expression patterns among individual cancer cells can be compared.
  • DNA microarrays can also be used to compare differences in gene expression between different classes of a trait. For example, by comparing the expression level of a gene before and after treatment with a drug in cells, or by comparing the expression level of a gene between a normal tissue and a diseased tissue, a gene showing a difference in expression level can be detected. there is.
  • the existing chromosomal testing method for detecting chromosomal abnormalities such as Down's syndrome and Turner's syndrome has a problem in that the diagnosis accuracy of diseases caused by minute chromosomal abnormalities is significantly lowered.
  • the present invention provides a data extraction module for generating first bacterial artificial chromosome expression ratio data by extracting expression ratio information for each bacterial artificial chromosome from the first microarray data; a normalization module for performing Royce normalization on the first bacterial artificial chromosome expression ratio data; a neural network module comprising an input layer, a hidden layer, and an output layer, receiving the first bacterial artificial chromosome expression ratio data and calculating class information of a characteristic to be classified; a decoding module for generating first classification class information including values of neurons of the output layer of the neural network module; a first loss value calculation module for calculating a first loss value by inputting first correct answer class information and the first classification class information into a softmax loss function; a second loss value calculation module for calculating a second loss value by inputting the first correct answer class information and the first classification class information into a root mean square error loss function; It provides a microarray specific determinant extraction system including a model design module for calculating a third loss
  • the data extraction module extracts expression ratio information for each bacterial artificial chromosome from the first microarray data, and the first bacterial artificial chromosome expression ratio data generating a; (S2) performing, by the normalization module, Royce normalization on the first bacterial artificial chromosome expression ratio data; (S3) converting, by the encoding module, the data format so that the first bacterial artificial chromosome expression ratio data corresponds to the neurons of the input layer of the neural network module; (S4) receiving, by the neural network module, the expression rate data of the first bacterial artificial chromosome, and calculating class information of a characteristic to be classified for each neuron of an output layer; (S5) generating, by a decoding module, first classification class information including values of neurons in an output layer of the neural network module; (S6) calculating, by the first loss value calculation module, the first correct answer class information and the first classification class information into a softmax loss function to calculate
  • the third loss value is calculated by linearly combining the first loss value calculated using the softmax loss function and the second loss value calculated using the root mean square error loss function, and then the neural network module is trained. can do.
  • the model design module searches for a bacterial artificial chromosome corresponding to a neuron in the input layer having the largest sum of first weights between neurons in the hidden layer in the neural network module learned according to the third loss value, and a chromosome affecting class classification It can be judged by the bacterial artificial chromosome, which is the determinant of the probe. Accordingly, it is possible to quickly determine the chromosomal probe that has the most influence for each characteristic, and the accuracy thereof can be improved.
  • FIG. 1 is a block diagram schematically illustrating a microarray-specific determinant factor extraction system according to an embodiment of the present invention.
  • FIG. 2 is a block diagram schematically illustrating microarray data input to a microarray-specific determinant factor extraction system according to an embodiment of the present invention.
  • FIG. 3 is a block diagram schematically illustrating a configuration included in a neural network module in a microarray-specific determinant factor extraction system according to an embodiment of the present invention.
  • FIG. 4 is a flowchart schematically illustrating a method for extracting a microarray specific determinant according to an embodiment of the present invention.
  • 1 is a block diagram schematically illustrating a microarray-specific determinant factor extraction system according to an embodiment of the present invention.
  • 2 is a block diagram schematically illustrating microarray data input to a microarray-specific determinant factor extraction system according to an embodiment of the present invention.
  • 3 is a block diagram schematically illustrating a configuration included in a neural network module in a microarray-specific determinant factor extraction system according to an embodiment of the present invention.
  • the microarray specific determinant extraction system 100 includes an input/output module 111 , a storage module 112 , a data extraction module 121 , a normalization module 122 , and an encoding A module 130, a neural network module 140, a decoding module 150, a first loss value calculation module 161, a second loss value calculation module 162, a model design module 170, It may include a correction value extraction module 180 and a visualization module 190 .
  • the microarray specific determinant extraction system 100 can process a microarray including a bacterial artificial chromosome (BAC) as a probe as a target.
  • BAC bacterial artificial chromosome
  • the input/output module 111 may receive the first microarray data MA1 and the second microarray data MA2 from the outside of the microarray specific determinant extraction system 100 .
  • the first microarray data MA1 may be training data, and bacterial artificial chromosome (BAC) information (MA1-B, hereinafter “first bacterial artificial chromosome information”) and positive bacterial artificial chromosome (BAC) information. or negative expression ratio information (MA1-R, hereinafter “first expression ratio information”) and correct answer class information (MA1-C, hereinafter “first correct answer class information”).
  • BAC bacterial artificial chromosome
  • first BAC positive bacterial artificial chromosome
  • MA1-R negative expression ratio information
  • MA1-C correct answer class information
  • the first bacterial artificial chromosome information includes a bacterial artificial chromosome identifier (MA1-Bi), a position at which the bacterial artificial chromosome is arranged on the microarray (MA1-Bp), and genetic information (MA1-Bp) of the bacterial artificial chromosome. Bg) may be included.
  • the first correct answer class information MA1-C may be correct answer data corresponding to the first expression rate information MA1-R.
  • the second microarray data may be verification data or general data used after verification, and includes bacterial artificial chromosome (BAC) information (MA2-B, hereinafter “second bacterial artificial chromosome information”) and bacterial artificial chromosome (BAC) positive or negative expression ratio information (MA2-R, hereinafter “second expression ratio information”) and correct answer class information in which probability information is defined in advance for each class of the characteristic to be classified (MA2) -C, hereinafter “second correct answer class information”) may be included.
  • BAC bacterial artificial chromosome
  • MA2-R bacterial artificial chromosome positive or negative expression ratio information
  • second expression ratio information correct answer class information in which probability information is defined in advance for each class of the characteristic to be classified
  • the second bacterial artificial chromosome information includes a bacterial artificial chromosome identifier (MA2-Bi), a position where the bacterial artificial chromosome is arranged on the microarray (MA2-Bp), and genetic information of the bacterial artificial chromosome (MA2-Bi). Bg) may be included.
  • the second correct answer class information MA2-C may be correct answer data corresponding to the second expression rate information MA2-R.
  • the input/output module 111 is a microarray specific determinant extraction system 100 and a personal area network (PAN), a local area network (LAN), a metropolitan area network (Metropolitan Area Network, MAN), a wide area network Protocols such as Transmission Control Protocol/Internet Protocol (TCP/IP), Server Message Block (SMB), Common Internet File System (CIFS), and Network File System (NFS) from other computing devices connected by a Wide Area Network (WAN).
  • PAN personal area network
  • LAN local area network
  • MAN metropolitan area network
  • TCP/IP Transmission Control Protocol/Internet Protocol
  • SMB Server Message Block
  • CIFS Common Internet File System
  • NFS Network File System
  • the input/output module 110 includes a serial port, a parallel port, a Small Computer System Interface (SCSI), a Universal Serial Bus (USB), an IEEE 1394, an Advanced Technology Attachment (ATA), and a Serial Advanced (SATA).
  • the first microarray data MA1 and the second microarray data MA2 may be transmitted from a data input/output terminal such as a technology attachment) or a peripheral device connected to another data input/output terminal.
  • the storage module 112 may store all data input to the microarray-specific determinant factor extraction system 100 or generated by the microarray-specific determinant factor extraction system 100 .
  • the remaining modules except the storage module 112 load all data stored in the storage module 112 and can be used
  • the storage module 112 may include a storage device to store data.
  • Storage devices include hard disk drives, optical disc drives, magnetic tapes, floppy disks, flash memory, solid state drives (SSDs), and the like. It may be a non-volatile memory device or a volatile memory device such as a random access memory (RAM), but is not limited thereto and may be a different type of memory device.
  • RAM random access memory
  • the data extraction module 121 may extract expression ratio information for each bacterial artificial chromosome (BAC) from the microarray data.
  • BAC bacterial artificial chromosome
  • the data extraction module 121 extracts the first bacterial artificial chromosome information (MA1-B) and the corresponding first expression ratio information (MA1-R) from the first microarray data (MA1) to be paired.
  • the data extraction module 121 extracts the bacterial artificial chromosome identifier (MA1-Bi) and the first expression ratio information (MA1-R) included in the first bacterial artificial chromosome information (MA1-B), By pairing, the first bacterial artificial chromosome expression ratio data (R1) can be generated.
  • the data extraction module 121 extracts the second bacterial artificial chromosome information (MA2-B) and the corresponding second expression ratio information (MA2-R) from the second microarray data (MA2) to be paired.
  • the data extraction module 121 pairs the bacterial artificial chromosome identifier (MA2-Bi) included in the second bacterial artificial chromosome information (MA2-B) with the second expression ratio information (MA2-R).
  • the second bacterial artificial chromosome expression ratio data (R2) can be generated.
  • the data extraction module 121 may generate expression ratio data for each bacterial artificial chromosome so that other modules can easily obtain expression ratio information for each bacterial artificial chromosome (BAC).
  • the normalization module 122 may perform normalization on the expression ratio data for each bacterial artificial chromosome generated by the data extraction module 121 .
  • the normalization module 122 may perform Lowess normalization so that the first bacterial artificial chromosome expression ratio data R1 and the second bacterial artificial chromosome expression ratio data R2 can maintain continuity, respectively.
  • the encoding module 130 may convert the expression ratio data for each bacterial artificial chromosome into a data format that the neural network module 140 can process.
  • the encoding module 130 may convert the data format of the first bacterial artificial chromosome expression ratio data R1 normalized by the normalization module 122 to correspond to the neurons of the input layer included in the neural network module 140 . there is.
  • the encoding module 130 may convert the data format of the second bacterial artificial chromosome expression ratio data R2 normalized by the normalization module 122 to correspond to the neurons of the input layer included in the neural network module 140 . there is.
  • the neural network module 140 may calculate class information of a characteristic to be classified according to the expression ratio data for each bacterial artificial chromosome.
  • the neural network module 140 may include an input layer 141 , a hidden layer 142 , and an output layer 143 .
  • the neural network module 140 may include one or more hidden layers 142 , and the hidden layers 142 may be located between the input layer 141 and the output layer 143 .
  • the input layer 141 may include one or more neurons 141n.
  • the input layer 141 may include as many neurons as the number of bacterial artificial chromosome identifiers (MA1-Bi, MA2-Bi).
  • the neural network module 140 is the first bacterial artificial chromosome expression ratio data R1 in which the data format is converted in the encoding module 130 after being normalized in the normalization module 122, respectively, in the neurons 141n of the input layer 141 Alternatively, the second bacterial artificial chromosome expression ratio data (R2) may be input.
  • the hidden layer 142 may include one or more neurons 142n. Each neuron of the hidden layer 141 may correspond to all neurons of the input layer 141 .
  • a first weight W1 may be included as relationship information between the neurons 141n of the input layer 141 and the neurons 142n of the hidden layer 142 .
  • the neural network module 140 multiplies the value of each neuron 141n of the input layer 141 corresponding to the neuron 142n of the hidden layer 142 by a first weight W1 therebetween, and then returns the multiplied value By adding them all together, the value of the neuron 142n of the hidden layer 142 may be calculated.
  • the output layer 143 may include one or more neurons 143n.
  • the output layer 143 may include as many neurons 143n as the number of classes of characteristics to be classified.
  • Each of the neurons 143n of the output layer 143 may correspond to all neurons 142n of the hidden layer 142 .
  • a second weight W2 may be included as relationship information between the neurons 142n of the hidden layer 142 and the neurons 143n of the output layer 143 .
  • the neural network module 140 multiplies the value of each neuron 142n of the hidden layer 142 corresponding to the neuron 143n of the output layer 143 and a second weight W2 therebetween, and then adds all of the multiplied values. In addition, the value of the neuron 143n of the output layer 143 may be calculated.
  • the neural network module 140 after the first bacterial artificial chromosome expression ratio data (R1) or the second bacterial artificial chromosome expression ratio data (R2) is input, the class information of the characteristic to be classified, the probability for each class of the output layer It can be calculated for each neuron 143n.
  • the decoding module 150 may convert the data format of the value of the neuron 143n of the output layer so as to calculate the value of the loss function.
  • the decoding module 150 after being normalized by the normalization module 122, is calculated when inputting the first bacterial artificial chromosome expression ratio data R1 whose data format is converted in the encoding module 130 to the neural network module 140
  • the first classification class information C1 may be generated including the values of all neurons 143n of the output layer 143 .
  • the decoding module 150 is calculated when inputting to the neural network module 140 the second bacterial artificial chromosome expression ratio data R2, which is normalized in the normalization module 122 and then converted in the data format in the encoding module 130
  • the second classification class information C2 may be generated by including the values of all neurons 143n of the output layer 143 .
  • the first loss value calculation module 161 and the second loss value calculation module 162 may respectively calculate the value of the loss function by using the class information classified by the neural network module 140 and the correct answer class information.
  • the first loss value calculation module 161 applies the first classification class information C1 and the first correct answer class information MA1-C included in the first microarray data MA1 to the softmax according to Equation 1 below.
  • the first loss value L1 may be calculated by inputting the (softmax) loss function.
  • Equation 1 x i and x j represent the first classification class information C1.
  • N represents the total number of the first classification class information C1, which is the number of neurons 143n in the output layer 143 .
  • yi represents the first correct answer class information MA1-C.
  • the first loss value calculation module 161 inputs the second classification class information C2 and the second correct answer class information MA2-C included in the second microarray data MA2 into Equation 1, One loss value L1 can be calculated.
  • x i and x j represent the second classification class information (C2).
  • N represents the total number of the second classification class information C2, which is the number of neurons 143n in the output layer 143 .
  • y i represents the second correct answer class information MA2-C.
  • the second loss value calculation module 162 calculates the first classification class information C1 and the first correct answer class information MA1-C included in the first microarray data MA1 as a root mean square according to Equation 2 below.
  • the second loss value L2 may be calculated by inputting the root mean square error (RMSE) into the loss function.
  • RMSE root mean square error
  • N represents the total number of the first classification class information C1, which is the number of neurons 143n in the output layer 143 .
  • y i represents the first correct answer class information MA1-C.
  • the second loss value calculation module 162 inputs the second classification class information (C2) and the second correct answer class information (MA2-C) included in the second microarray data (MA2) into Equation (2), 2
  • the loss value L2 can be calculated.
  • _ represents the second classification class information (C2).
  • N represents the total number of the second classification class information C2, which is the number of neurons 143n in the output layer 143 .
  • yi represents the second correct answer class information MA2-C.
  • the model design module 170 may calculate a third loss value L3 by performing a linear combination of the first loss value L1 and the second loss value L2 .
  • the model design module 170 may calculate the third loss value L3 by inputting the first loss value L1 and the second loss value L2 to the linear combination function according to Equation 3 below.
  • a 1 and A 2 are parameters of the linear combination function, and may be determined in advance according to the weights of the first loss value L1 and the second loss value L2. For example, the sum of A 1 and A 2 may be 1.
  • the model design module 170 may back propagate the third loss value L3 to update the first weight W1 and the second weight W2 of the neural network module 140 .
  • the model design module 170 analyzes the first weight W1 between the neurons 141n of the input layer 141 and the neurons 142n of the hidden layer 142 after the neural network learning is finished, thereby affecting class classification. Mitch can search for bacterial artificial chromosomes on microarrays. For example, the model design module 170 adds a first weight W1 between each neuron 142n of the hidden layer 142 with respect to each neuron 141n of the input layer 141, and then the summed A neuron 141n of the input layer 141 having the largest first weight W1 may be searched for.
  • the model design module 170 the bacterial artificial chromosome identifier of the first bacterial artificial chromosome expression ratio data R1 corresponding to the neurons 141n of the input layer 141 having the largest sum of the first weights W1
  • the bacterial artificial chromosome (BAC) corresponding to (MA1-Bi) can be determined as the bacterial artificial chromosome (D-BAC), the determinant that most affects the characteristics to be classified.
  • the correction value extraction module 180 may correct the first weight W1 when the model design module 170 searches for the bacterial artificial chromosome, which is a determining factor affecting class classification.
  • Correction value extraction module 180 the first weight W1 between the neurons 141n of the input layer 141 and the neurons 142n of the hidden layer 142 according to the change of the first microarray data MA1 By analyzing the change, the correction value may be reflected in the first weight W1.
  • the visualization module 190 may visually display results calculated by other modules.
  • the visualization module 190 provides first classification class information (C1) according to the first bacterial artificial chromosome expression ratio data (R1) and second classification class information (C2) according to the second bacterial artificial chromosome expression ratio data (R2) can be displayed individually.
  • the visualization module 190 may display the bacterial artificial chromosome (D-BAC), a determinant that most affects the characteristics to be classified.
  • D-BAC bacterial artificial chromosome
  • FIG. 2 is a flowchart schematically illustrating a method for extracting a microarray specific determinant according to an embodiment of the present invention.
  • the data extraction module 121 performs the expression ratio for each bacterial artificial chromosome (BAC) in the microarray data. This is the step of extracting information.
  • the data extraction module 121 extracts one or more first bacterial artificial chromosome information (MA1-B) and the corresponding first expression ratio information (MA1-R) from the first microarray data (MA1) to make a pair can be built
  • the data extraction module 121 extracts the bacterial artificial chromosome identifier (MA1-Bi) and the first expression ratio information (MA1-R) included in the first bacterial artificial chromosome information (MA1-B), By pairing, the first bacterial artificial chromosome expression ratio data (R1) can be generated.
  • the second step ( S2 ) is a step in which the normalization module 122 performs normalization on the expression ratio data for each bacterial artificial chromosome generated by the data extraction module 121 .
  • the normalization module 122 may perform Lowess normalization so that the first bacterial artificial chromosome expression ratio data R1 can maintain continuity.
  • the third step (S3) is a step in which the encoding module 130 converts the expression ratio data for each bacterial artificial chromosome into a data format that the neural network module 140 can process.
  • the encoding module 130 converts the first bacterial artificial chromosome expression ratio data R1 normalized by the normalization module 122 to correspond to the neurons 141n of the input layer 141 included in the neural network module 140 . You can convert data types.
  • the fourth step S4 is a step in which the neural network module 140 calculates a value for each class of the characteristic to be classified.
  • the neural network module 140 includes a first weight W1 between the neurons 142n of the hidden layer 142 and the neurons 141n of the input layer 141 , and the neurons 143n and the hidden layer 142 of the output layer 143 . ), the second weight W2 between the neurons 142n may be randomly initialized.
  • the neural network module 140 may input the first bacterial artificial chromosome expression ratio data R1 to the neurons 141n of the input layer 141 , respectively.
  • the neural network module 140 multiplies the value of each neuron 141n of the input layer 141 corresponding to the neuron 142n of the hidden layer 142 by a first weight W1 therebetween, and then returns the multiplied value By adding them all together, the value of the neuron 142n of the hidden layer 142 may be calculated.
  • the neural network module 140 multiplies the value of each neuron 142n of the hidden layer 142 corresponding to the neuron 143n of the output layer 143 and a second weight W2 therebetween, and then adds all of the multiplied values. In addition, the value of the neuron 143n of the output layer 143 may be calculated.
  • Each value of the neuron 143n of the output layer 143 may be a class probability, which is class information of a characteristic to be classified.
  • a fifth step ( S5 ) is a step in which the decoding module 150 converts the data format of the value of the neuron 143n of the output layer 143 .
  • the decoding module 150 may generate the first classification class information C1 including values of all neurons of the output layer 143 .
  • the sixth step S6 is a step in which the first loss value calculation module 161 calculates the first loss value L1.
  • the first loss value calculation module 161 converts the first classification class information C1 and the first correct answer class information MA1-C included in the first microarray data MA1 to the softmax of Equation 1 ) can be input to the loss function to calculate the first loss value L1.
  • the seventh step S7 is a step in which the second loss value calculation module 162 calculates the second loss value L2.
  • the second loss value calculation module 162 calculates the root mean square error of Equation 2 based on the first classification class information C1 and the first correct answer class information MA1-C included in the first microarray data MA1. RMSE) may be input to the loss function to calculate the second loss value L2.
  • the eighth step S8 is a step in which the model design module 170 calculates the third loss value L3.
  • the model design module 170 may input the first loss value L1 and the second loss value L2 to the linear combination function of Equation 3 to calculate the third loss value L3.
  • the ninth step S9 is a step in which the model design module 170 updates the first weight W1 and the second weight W2 of the neural network module 140 .
  • the model design module 170 may back propagate the third loss value L3 to update the first weight W1 and the second weight W2 of the neural network module 140 .
  • the tenth step (S10) may be performed. If the third loss value L3 does not converge, the fourth step S4 to the ninth step S9 may be repeatedly executed.
  • the model design module 170 may search for bacterial artificial chromosomes in the microarray that affect class classification.
  • the model design module 170 adds a first weight W1 between each neuron of the hidden layer 142 with respect to each neuron of the input layer 141, and then the summed first weight W1 has the largest value.
  • a neuron 141n of the input layer 141 may be searched for.
  • the model design module 170 is configured to configure the bacterial artificial chromosome identifier MA1 of the first bacterial artificial chromosome expression ratio data R1 corresponding to the neurons 141n of the input layer 141 having the largest sum first weight W1. -Bi), it can be determined as the bacterial artificial chromosome (D-BAC), the determinant that most affects the characteristics to be classified.
  • D-BAC bacterial artificial chromosome

Abstract

La présente invention concerne un système d'extraction d'une sonde chromosomique utilisée lors de la classification d'une classe spécifique dans un microréseau. La présente invention permet de calculer une troisième valeur de perte en effectuant une combinaison linéaire d'une première valeur de perte calculée à l'aide d'une fonction de perte de softmax et une deuxième valeur de perte calculée à l'aide d'une fonction de perte d'erreur quadratique moyenne, et de mettre à jour un premier poids et un second poids d'un module de réseau neuronal. De plus, la présente invention permet de rechercher un chromosome artificiel bactérien correspondant à un neurone dans une couche d'entrée ayant la plus grande somme de premiers poids parmi les neurones dans une couche cachée, dans le module de réseau neuronal appris à l'aide de la troisième valeur de perte, et de le déterminer en tant que chromosome artificiel bactérien déterminant, qui est une sonde chromosomique qui affecte une classification de classe.
PCT/KR2021/014237 2020-10-19 2021-10-14 Système d'extraction de déterminant spécifique de microréseau basé sur l'intelligence artificielle WO2022086053A1 (fr)

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