Apparatus, method and program

Abstract

To provide an apparatus, a method and a program.SOLUTION: An apparatus comprises: a detection part which is severally provided at unique positions in a biochip, and detects a position of a probe hybridized with nucleic acid included in a sample out of a plurality of probes in which at least part of probes has base sequences different from each other; and a learning processing part which, by using learning data including a pattern of a position of the probe which was hybridized with nucleic acid included in the sample and detected by the detection part and a type of an organism having nucleic acid included in the sample, performs learning processing of a model which outputs the type of an organism having the nucleic acid included in the sample corresponding to newly inputting a pattern of a position of the probe hybridized with nucleic acid included in the sample.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an apparatus, a method, and a program.

Background Art

[0002] Patent Document 1 (for example, paragraphs 0215, 0316) etc. describe "1. Design, synthesize, and purify a fluorescent probe for target RNA. 2. Use Biodot to print the probe (1 nM) and a surfactant (for example, Zwittergent) on an X plate. 3. Apply one drop of blood to a substrate plate, close and press the chip, and incubate at room temperature for 1 minute. 4. Insert the chip into the device, take a photo, and analyze the data.", "The printed probe and surfactant (for example, Zwittergent) are dissolved in the blood. The surfactant (for example, Zwittergent) lyses red blood cells and permeabilizes white blood cells to promote the probe entering the cells to bind to the target RNA." [Prior Art Documents] [Patent Documents] [Patent Document 1] Japanese Patent Application Publication No. 2021-536019 [Patent Document 2] Japanese Patent Application Publication No. 2013-510579 [Patent Document 3] Japanese Patent Application Publication No. 2018-508228 [Patent Document 4] International Publication No. 2019 / 43779 [Patent Document 5] U.S. Patent No. 5624487 [Patent Document 6] U.S. Patent No. 6439810 [Patent Document 7] U.S. Patent No. 5928906 [Patent Document 8] Japanese Unexamined Patent Application Publication No. 2015-42152 [Non-Patent Document 1] Takanuma et al., "Development of Nucleic Acid Detection Method for Rapid Microbial Inspection", Yokogawa Technical Report Vol. 60, 2017, Internet <URL:https: / / web-material3.yokogawa.com / 19 / 13323 / tabs / rd-tr-r06001-002.jp.pdf>

Summary of the Invention

[0003] In a first aspect of the present invention, there is provided an apparatus including: a detection unit that detects positions of probes that hybridize with nucleic acids contained in a sample among a plurality of probes provided at respective specific positions in a biochip and having at least some probes with different base sequences from each other; and a learning processing unit that performs learning processing of a model that outputs the type of organism having the nucleic acids contained in the sample in response to a pattern of positions of the probes that hybridize with the nucleic acids contained in the sample being newly input, using learning data including the pattern of positions of the probes detected by the detection unit after hybridizing with the nucleic acids contained in the sample and the type of organism having the nucleic acids contained in the sample.

[0004] In the above apparatus, the learning processing unit may perform separate learning processing of the above models for each type of the biochip in which at least one of the base sequences of the probes or the specific positions of the probes is different.

[0005] Any of the above apparatuses may further include: a supply unit that supplies a pattern of positions of probes that are newly detected by the detection unit after hybridizing with nucleic acids contained in one sample to the model; and a specifying unit that specifies, as the type of organism having the nucleic acids contained in the one sample, the type of organism output from the model in response to the pattern of positions of the probes being supplied by the supply unit.

[0006] In a second aspect of the present invention, among a plurality of probes provided at respective unique positions within a biochip, at least some of which have different base sequences from each other, a detection unit that detects the positions of the probes hybridized with the nucleic acid contained in a sample, and a model that outputs the type of organism having the nucleic acid contained in the sample in response to the input of the pattern of the positions of the probes hybridized with the nucleic acid contained in the sample, a supply unit that supplies the pattern of the positions of the probes detected by the detection unit to the model, and a specifying unit that specifies, as the type of organism having the nucleic acid contained in the one sample, the type of organism output from the model in response to the supply of the pattern of the positions of the probes by the supply unit, are provided.

[0007] In any of the above devices including the specifying unit, the supply unit may supply the pattern of the positions of the probes detected by the detection unit to the model corresponding to the biochip in which the positions of the probes are detected by the detection unit, among a plurality of the models that are different for each type of the biochip in which at least one of the base sequences of the probes or the unique positions of the probes is different.

[0008] In any of the above devices, at least some of the plurality of probes may have a base sequence common to a plurality of types of organisms.

[0009] In any of the above devices, the plurality of probes may each have a base sequence with 20 or fewer bases.

[0010] In a third aspect of the present invention, among a plurality of probes provided at respective unique positions within a biochip and having at least some probes with different base sequences, a detection step of detecting the positions of the probes hybridized with the nucleic acid contained in the sample, a learning data including the pattern of the positions of the probes hybridized with the nucleic acid contained in the sample and the type of the organism having the nucleic acid contained in the sample, and a learning process step of performing a learning process of a model that outputs the type of the organism having the nucleic acid contained in the sample in response to a new input of the pattern of the positions of the probes hybridized with the nucleic acid contained in the sample are provided.

[0011] In a fourth aspect of the present invention, among a plurality of probes provided at respective unique positions within a biochip and having at least some probes with different base sequences, a detection step of detecting the positions of the probes hybridized with the nucleic acid contained in one sample, a supply step of supplying the pattern of the positions of the probes detected by the detection step to a model that outputs the type of the organism having the nucleic acid contained in the sample in response to the input of the pattern of the positions of the probes hybridized with the nucleic acid contained in the sample, and a specifying step of specifying, as the type of the organism having the nucleic acid contained in the one sample, the type of the organism output from the model in response to the supply of the pattern of the positions of the probes by the supply step are provided.

[0012] In a fifth aspect of the present invention, a computer is caused to function as a learning processing unit that performs learning processing of a model that, in response to a pattern of positions of probes hybridized with a nucleic acid contained in a sample being newly input, outputs a type of organism having the nucleic acid contained in the sample, using learning data including a detection unit that detects positions of probes hybridized with the nucleic acid contained in the sample among a plurality of probes provided at respective unique positions in a biochip and having at least some probes with different base sequences from each other, a pattern of positions of probes hybridized with the nucleic acid contained in the sample, and a type of organism having the nucleic acid contained in the sample.

[0013] In a sixth aspect of the present invention, a computer is caused to function as a supply unit that supplies a pattern of positions of probes detected by a detection unit to a model that outputs a type of organism having a nucleic acid contained in a sample in response to a pattern of positions of probes hybridized with the nucleic acid contained in the sample being input, and a specifying unit that specifies, as the type of organism having the nucleic acid contained in the one sample, the type of organism output from the model in response to the pattern of positions of probes being supplied by the supply unit.

[0014] Note that the above summary of the invention does not list all the necessary features of the present invention. Also, sub-combinations of these feature groups can also be inventions.

Brief Description of the Drawings

[0015]

Figure 1

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Figure 5

Mode for Carrying Out the Invention

[0016] Hereinafter, the present invention will be described through embodiments of the invention. However, the following embodiments do not limit the invention according to the claims. Also, not all combinations of features described in the embodiments are essential for the solution means of the invention.

[0017] <1. Device> FIG. 1 shows a device 1 according to this embodiment. The device 1 may perform a learning process of a model 141 for identifying the types of microorganisms mixed in foods and beverages. In addition to, or instead of, this, the types of microorganisms mixed in foods and beverages may be identified using the model 141. The device 1 includes an injection unit 11, a detection unit 12, a learning data acquisition unit 13, a storage unit 14, a learning processing unit 15, a supply unit 16, and an identification unit 17.

[0018] The microorganisms of a specific target, as an example, include the genera Acinetobacter, Actinomyces, Aerococcus, Aeromonas, Alcaligenes, Bacillus, Bacteriodes, Bordetella, Branhamella, Brevibacterium, Campylobacter, Candida, Capnocytophaga, Chromobacterium, Clostridium, Corynebacterium, Cryptococcus, Deinococcus, Enterococcus, Erysipelothrix, Escherichia, Flavobacterium, Gemella, Haemophilus, Klebsiella, Lactobacillus, Lactococcus, Legionella, Leuconostoc, Listeria, Micrococcus, Mycobacterium, Neisseria, Cryptosporidium, Nocardia, Oerskovia, Paracoccus, Pediococcus, Peptostreptococcus, Propionibacterium, Proteus, Pseudomonas, Rahnella, Rhodococcus.It can be selected from the group consisting of the genus Rhodospirillium, the genus Staphylococcus, the genus Streptomyces, the genus Streptococcus, the genus Vibrio, and the genus Yersinia. There are microorganisms that take forms such as spores and cysts in oligotrophic states, regardless of the state of the cells due to such growth states.

[0019] <1.1. Injection unit 11> The injection unit 11 injects a sample into the biochip 100. The sample may contain nucleic acids (such as genomic DNA, ribosomal RNA, plasmid DNA, etc.) of any microorganism of a specific target. The sample may be generated from food and beverages by conventionally known methods. For example, the sample may be generated by collecting the microorganisms contained in the sample extracted from food and beverages by filtration or centrifugation and extracting the nucleic acids of the microorganisms. The collected microorganisms may be cultured and then subjected to nucleic acid extraction. As a method for extracting nucleic acids from microorganisms, the methods described in International Publication No. 2019 / 43779 and Patent No. 5624487, so-called dHTP method, may be used. As an example, the microorganisms captured by a filter may be housed in a container together with the filter and exposed to high temperature conditions to destroy the membrane structure of the microorganisms and extract the nucleic acids. The extracted nucleic acids may be amplified by the polymerase chain reaction (PCR) method.

[0020] The injection unit 11 according to this embodiment may inject a sample into the same type of biochip 100. The biochip 100 has a plurality of probes inside. Among these plurality of probes, at least some of the probes may have different base sequences from each other. As an example, all the probes of the biochip 100 may each have a base sequence different from other probes, or some of the probes of the biochip 100 may have the same base sequence as other probes.

[0021] At least a part of the plurality of probes included in the biochip 100 may have a nucleotide sequence common to a plurality of types of microorganisms. When the biochip 100 has probes with nucleotide sequences that are not common to a plurality of types of microorganisms, the nucleotide sequence may be a nucleotide sequence specific to a specific target microorganism or may be a nucleotide sequence not included in the specific target microorganism. However, at the design stage of the biochip 100, the nucleotide sequence of each probe may be determined independently of the nucleotide sequence of the nucleic acid of the specific target microorganism. As an example, as long as the nucleotide sequences of the respective probes are the same among the biochips 100 of the same type, the nucleotide sequences of the respective probes may be determined randomly. Each probe may include a nucleotide sequence having 20 or fewer bases. The number of probes having different nucleotide sequences may be, for example, 100 or more.

[0022] Each probe may hybridize with the corresponding nucleotide sequence. At least some of the plurality of probes may have a nucleotide sequence complementary to at least a part of the nucleotide sequence of the nucleic acid of the specific target microorganism and may specifically hybridize with the nucleic acid of the corresponding nucleotide sequence.

[0023] The biochip 100 may further include a positive control probe that emits a signal detected by the detection unit 12 independently of the nucleic acid contained in the sample. The positive control probe may not hybridize with the nucleic acid.

[0024] Each probe and the positive control probe may be provided at a unique position within the same type of biochip 100. For example, probes at the same position in the same type of biochip 100 may have the same nucleotide sequence. In this embodiment, as an example, the biochip 100 may have a plurality of probes and one or more positive control probes on the inner surfaces of two opposed transparent substrates. The biochip 100 may have an injection port for injecting a sample therein and a discharge port for discharging the liquid inside.

[0025] At least one of the probe of the biochip 100 and the nucleic acid contained in the sample may be preliminarily attached with a label that emits a signal detectable by the detection unit 12 described later in the hybridized state. For example, the label may be attached to the probe and may emit a signal in response to the hybridization of the probe and the nucleic acid. Alternatively, the label may be attached to the nucleic acid of the sample and may emit a signal regardless of whether the nucleic acid hybridizes with the probe. In this case, after injecting the sample into the biochip 100, the nucleic acid that has not hybridized with the probe may be removed from the biochip 100, and then the signal may be detected. As the label, a fluorescent dye, a radioisotope, a paramagnetic isotope, an enzyme, or the like can be used. In the present embodiment, as an example, the label may be a fluorescent dye and may be attached to the probe. Further, the positive control probe may also be preliminarily attached with a label. The label of the positive control probe may always emit a signal.

[0026] Note that since at least some of the probes of the biochip 100 according to the present embodiment have base sequences common to a plurality of types of microorganisms, it may not be possible to identify the microorganisms (also referred to as contaminating microorganisms) having the nucleic acid contained in the sample only by checking the presence or absence of hybridization for individual probes. On the other hand, since at least some of the probes of the biochip 100 have different base sequences, the position pattern of the hybridized probes may differ depending on the type of contaminating microorganisms. Therefore, in the biochip 100 according to the present embodiment, by learning the position pattern of the hybridized probes and the type of contaminating microorganisms, it may be possible to identify the type of contaminating microorganisms corresponding to the position pattern of the hybridized probes.

[0027] <1.2. Detection unit 12> The detection unit 12 detects the positions of the probes that have hybridized with the nucleic acids contained in the sample among the plurality of probes on the biochip 100. The detection unit 12 may detect the positions of the hybridized probes based on the positions of the positive control probes. The detection unit 12 may detect the hybridized probes and the positive control probes by detecting the labels within the biochip 100. The detection unit 12 may detect the signal emitted from the label attached to either the hybridized probe or the nucleic acid. The detection unit 12 may further detect the signal emitted from the positive control probes. The detection unit 12 may detect the labels within the biochip 100 while being fixed in position with respect to the biochip 100. The detection unit 12 may be disposed opposite to the biochip 100 and detect the labels within the biochip 100 through the transparent substrate of the biochip 100. The detection unit 12 may supply data (also referred to as position pattern data) indicating the pattern of the positions of the detected labels to the learning data acquisition unit 13 and the supply unit 16. The position pattern data may include information indicating the intensity of the signal at each detection position. When the detection unit 12 detects fluorescence from the label as a signal, the position pattern data may include information indicating the wavelength of the signal at each detection position.

[0028] <1.3. Learning data acquisition unit 13> The learning data acquisition unit 13 acquires learning data including the pattern of the positions of the probes that have hybridized with the nucleic acids contained in the sample and detected by the detection unit 12, and the type of microorganism having the nucleic acids contained in the sample. The learning data may be data used in the learning of the model 141. The microorganism having the nucleic acids contained in the sample may be the microorganism from which the nucleic acids contained in the sample were extracted, or may be the microorganism contained in the food or beverage that was the source of the sample.

[0029] The learning data acquisition unit 13 may generate learning data by associating position pattern data indicating the position pattern of probes detected by hybridizing with the nucleic acid contained in the sample, and data indicating the type of microorganism from which the nucleic acid was extracted (also referred to as type data). The learning data acquisition unit 13 may acquire the position pattern data from the detection unit 12 and may acquire the type data from the operator. As an example, in the learning stage of the model 141, a sample containing the nucleic acid of an arbitrary microorganism may be generated by the operator and injected into the biochip 100, and the type data of the microorganism may be supplied from the operator to the learning data acquisition unit 13, and the position pattern data of the probe hybridized with the nucleic acid of the microorganism may be supplied from the detection unit 12 to the learning data acquisition unit 13 respectively. The learning data acquisition unit 13 may also acquire, from an operator or the like, learning data in which the position pattern data and the type data are associated and generated in advance. The learning data acquisition unit 13 may supply the acquired learning data to the storage unit 14.

[0030] <1.4. Storage unit 14> The storage unit 14 stores various information. The storage unit 14 may store the learning data file 140 and the model 141.

[0031] <1.4.1. Learning data file 140> The learning data file 140 stores learning data. In this embodiment, as an example, the learning data file 140 may store each learning data supplied from the learning data acquisition unit 13.

[0032] <1.4.2. Model 141> The model 141 outputs the type of contaminating microorganism in the sample in response to a new input of the pattern of the positions of the probes hybridized with the nucleic acid contained in the sample. In this embodiment, as an example, the model 141 may output the type of microorganism in response to the input of the position pattern data output from the detection unit 12. The model 141 may receive learning processing by the learning processing unit 15. As an example, the model 141 may be an image analysis engine.

[0033] <1.5. Learning processing unit 15> The learning processing unit 15 performs the learning process of the model 141. The learning processing unit 15 reads learning data from the learning data file 140 in the storage unit 14 and performs a learning process on the model 141 in the storage unit 14. The learning processing unit 15 may perform the learning process of the model 141 by machine learning such as deep learning.

[0034] <1.6. Supply unit 16> The supply unit 16 supplies the pattern of the positions of the probes newly detected by the detection unit 12 by hybridizing with the nucleic acid contained in a sample to the model 141. The supply unit 16 may supply the position pattern data supplied from the detection unit 12 to the model 141 on which the learning process has been performed by the learning processing unit 15. As a result, data indicating the types of contaminating microorganisms may be output from the model 141 to the specifying unit 17.

[0035] <1.7. Specifying unit 17> The specifying unit 17 specifies, as the types of contaminating microorganisms in the above-mentioned one sample, the types of microorganisms output from the model 141 in response to the supply of the position pattern data to the model 141 by the supply unit 16. The specifying unit 17 may output information indicating the types of contaminating microorganisms to the outside.

[0036] According to the above device 1, the learning process of the model 141 is performed using the learning data including the position pattern of the probes detected by hybridization with the nucleic acid contained in the sample and the types of microorganisms having the nucleic acid contained in the sample. Therefore, it is possible to generate a model 141 that outputs the types of contaminating microorganisms by inputting the position pattern of the hybridized probes. As a result, without preparing probes with unique base sequences for each microorganism, it is possible to specify the types of contaminating microorganisms by inputting the position pattern of the hybridized probes into the model 141, so that the cost of the biochip 100 can be reduced.

[0037] In addition, in response to the pattern of the positions of the probes newly detected by the detection unit 12 being supplied to the model 141, the type of microorganism output from the model 141 is specified as the type of contaminating microorganism. Therefore, by causing the detection unit 12 to detect the positions of the hybridized probes, the type of contaminating microorganism can be specified.

[0038] In addition, since at least a part of the plurality of probes of the biochip 100 have base sequences common to a plurality of types of microorganisms, the base sequences of the probes can be shortened as compared with the case where the plurality of probes have unique base sequences for each microorganism. Therefore, the cost of the biochip 100 can be reduced.

[0039] In addition, since the plurality of probes of the biochip 100 each have a base sequence with 20 or fewer bases, the cost of the biochip 100 can be reduced as compared with the case where the base sequence has more than 20 bases.

[0040] <2. Probes of the Biochip 100> FIG. 2 shows the probe 101 of the biochip 100 according to the present embodiment together with the transparent substrate 105 and the nucleic acid 110. The black arrow symbols in the figure indicate the probes or nucleic acids, and the tip side of the arrow indicates the 5'-end, and the base end side of the arrow indicates the 3'-end.

[0041] Each probe 101 may have a first probe 1011 and a second probe 1012 having complementary base sequences to each other. The first probe 1011 may be a fluorescent probe modified with a fluorescent dye 1013, and the second probe 1012 may be a quenching probe modified with a quenching substance 1014 that suppresses the emission of the fluorescent dye. As shown on the left side in the figure, when there is no target nucleic acid in the biochip 100, the first probe 1011 and the second probe 1012 hybridize with each other, so that the fluorescent dye 1013 of the first probe 1011 and the quenching substance of the second probe 1012 are close to each other, and the emission of the fluorescent dye 1013 of the first probe 1011 may be suppressed. As shown on the right side in the figure, when the target nucleic acid 110 is present in the biochip 100, at least one of the first probe 1011 and the second probe 1012 hybridizes with the target nucleic acid 110, so that the fluorescent dye 1013 of the first probe 1011 and the quenching substance 1014 of the second probe 1012 are separated, and the fluorescent dye 1013 of the first probe 1011 may emit light. In the biochip 100 having such a probe, after injecting a sample into the biochip 100, the detection of the label can be performed without removing nucleic acids or the like that have not hybridized with the probe 101 from the biochip 100. As the biochip 100 as described above, for example, those described in Patent No. 6439810, Patent No. 5928906, JP-A-2015-42152, and the following Document 1 can be used.

[0042] Document 1: Takanuma et al., "Development of Nucleic Acid Detection Method for Rapid Microbial Inspection", Yokogawa Technical Report Vol. 60, 2017, Internet <URL:https: / / web-material3.yokogawa.com / 19 / 13323 / tabs / rd-tr-r06001-002.jp.pdf>

[0043] <3. Operation of Device 1> Figure 3 shows the operation of device 1. Device 1 performs the learning process of model 141 by performing the processes of steps S11 to S23.

[0044] In step S11, the injection unit 11 injects a sample into the biochip 100. The sample to be injected may contain nucleic acids of one or more microorganisms arbitrarily selected by an operator from among specific target microorganisms.

[0045] In step S13, the detection unit 12 detects the positions of the probes among the plurality of probes on the biochip 100 that have hybridized with the nucleic acids contained in the sample. Thereby, the positions of the probes that have hybridized with the nucleic acids of the microorganisms contained in the sample among the probes having different base sequences from each other may be detected.

[0046] In step S15, the learning data acquisition unit 13 acquires learning data including the pattern of the positions of the probes that have hybridized with the nucleic acids contained in the sample and have been detected by the detection unit 12, and the types of microorganisms having the nucleic acids contained in the sample. The learning data acquisition unit 13 may acquire the position pattern data from the detection unit 12 and acquire the microorganism type data from the operator. The type data may indicate the type of a single microorganism or the types of a plurality of microorganisms.

[0047] In step S21, the learning data acquisition unit 13 determines whether the number of learning data has reached a reference number. The reference number may be arbitrarily set. If the number of learning data has not reached the reference number (step S21; No), the process may shift to step S11. If the number of learning data has reached the reference number (step S23; Yes), the process may shift to step S23.

[0048] In step S23, the learning processing unit 15 performs learning processing of the model 141 using the acquired learning data. Thereby, a model 141 is generated that outputs the types of one or more contaminating microorganisms in response to the input of the pattern of the positions of the hybridized probes.

[0049] Note that the learning processing unit 15 may perform performance evaluation of the model 141 by performing cross-validation. For example, after the learning processing unit 15 performs learning processing of the model 141 using some of the learning data in the storage unit 14, the learning processing unit 15 supplies the position pattern data of some other learning data to the model 141 and evaluates the model 141 based on the degree of coincidence between the types of microorganisms output and the types of microorganisms indicated by the type data. The learning processing unit 15 may complete the learning processing of the model 141 when the degree of coincidence is equal to or higher than a reference value. The learning processing unit 15 may shift the processing to step S11 when the degree of coincidence is less than the reference value, or may output an error signal and end the processing. The error signal may prompt the operator to repeat the operations of steps S11 to S23 for a new model 141 using other types of biochips.

[0050] FIG. 4 shows other operations of the apparatus 1. The apparatus 1 identifies the contaminating microorganisms using the model 141 for which the learning processing has been performed by performing the processing of steps S31 to S37.

[0051] In step S31, the injection unit 11 injects one sample into the biochip 100. The sample to be injected may contain nucleic acids of one or more microorganisms among the specific target microorganisms.

[0052] In step S33, the detection unit 12 detects the positions of the probes that have hybridized with the nucleic acids contained in one sample among the plurality of probes of the biochip 100 in the same manner as in step S13.

[0053] In step S35, the supply unit 16 supplies the pattern of the positions of the probes detected in step S33 by the detection unit 12 to the model 141. Thereby, data indicating the types of one or more contaminating microorganisms may be output from the model 141.

[0054] In step S37, the specifying unit 17 specifies the type of microorganism output from the model 141 as the type of contaminating microorganism in a single sample. The specifying unit 17 may output information indicating the type of contaminating microorganism to the outside. When the processing of step S37 is completed, the apparatus 1 may complete the operation or may transfer the processing to step S31 described above.

[0055] <4. Modification Example> In the above-described embodiment, the apparatus 1 has been described as including the injection unit 11, the learning data acquisition unit 13, the storage unit 14, the learning processing unit 15, the supply unit 16, and the specifying unit 17, but it may not include any of these. For example, when the apparatus 1 does not include the supply unit 16 and the specifying unit 17, the learning processing unit 15 may perform learning of the model 141 and output the learned model 141. Further, when the apparatus 1 does not include the learning processing unit 15, the contaminating microorganism may be specified using the learned model 141. Also, when the apparatus 1 does not include the storage unit 14, learning processing may be performed on the model 141 in an externally connected storage device, or the contaminating microorganism may be specified using the model 141 in the externally connected storage device.

[0056] Also, although the injection unit 11 has been described as injecting a sample into the same type of biochip 100, the sample may be injected into a plurality of types of biochips 100 in which at least one of the base sequence of the probe or the unique position of the probe is different. In this case, the storage unit 14 may store different models 141 and learning data files 140 for each type of biochip 100, and the learning data acquisition unit 13 may acquire the type of the biochip 100 used from the operator and store the learning data in the learning data file 140 corresponding to the type of the biochip 100 used.

[0057] Also, when a sample is injected into a plurality of types of biochips 100, the learning processing unit 15 may perform learning processing of separate models 141 for each type of biochip 100. Thereby, unlike the case where learning processing of the same model 141 is performed using different types of biochips 100, it is possible to prevent a decrease in learning accuracy. Also, organisms in the sample can be identified using a variety of biochips 100.

[0058] Also, when a sample is injected into a plurality of types of biochips 100, the supply unit 16 may supply position pattern data to the model 141 corresponding to the biochip 100 in which the position of the probe is detected by the detection unit 12, that is, the biochip 100 that has been used, among the plurality of different models 141 for each type of biochip 100. Thereby, microorganisms in the sample can be identified using a variety of biochips 100.

[0059] Also, although the model 141 has been described as outputting the types of microorganisms having nucleic acids contained in the sample, it may output the types of one or more organisms other than microorganisms. In this case, the organisms to be identified may be animals, insects, plants, mycoplasmas, viruses, etc.

[0060] Also, various embodiments of the present invention may be described with reference to flowcharts and block diagrams, where the blocks may represent (1) stages of a process in which an operation is performed or (2) sections of an apparatus having a role of performing an operation. Specific stages and sections may be implemented by a dedicated circuit, a programmable circuit supplied with computer-readable instructions stored on a computer-readable medium, and / or a processor supplied with computer-readable instructions stored on a computer-readable medium. The dedicated circuit may include digital and / or analog hardware circuits, and may include an integrated circuit (IC) and / or discrete circuits. The programmable circuit may include reconfigurable hardware circuits including memory elements such as logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, flip-flops, registers, field programmable gate arrays (FPGA), programmable logic arrays (PLA), etc.

[0061] A computer-readable medium may include any tangible device capable of storing instructions executable by an appropriate device, and as a result, a computer-readable medium having instructions stored therein will comprise a product including instructions executable to create means for performing the operations specified in a flowchart or block diagram. Examples of computer-readable media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable media may include floppy (registered trademark) disks, diskettes, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), electrically erasable programmable read only memory (EEPROM), static random access memory (SRAM), compact disk read only memory (CD-ROM), digital versatile disk (DVD), Blu-ray (RTM) disk, memory stick, integrated circuit card, etc.

[0062] Computer-readable instructions may include any combination of one or more programming languages, including assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source code or object code written in object-oriented programming languages such as Smalltalk®, JAVA®, C++, and conventional procedural programming languages such as the "C" programming language or similar programming languages.

[0063] Computer-readable instructions may be provided to a processor or programmable circuitry of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus locally or via a wide area network (WAN) such as a local area network (LAN), the Internet, etc., and executed to create means for performing the operations specified in a flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0064] FIG. 5 shows an example of a computer 2200 in which multiple aspects of the present invention may be embodied, in whole or in part. Programs installed on the computer 2200 can cause the computer 2200 to function as an operation associated with the apparatus according to an embodiment of the present invention or as one or more sections of the apparatus, or execute the operation or the one or more sections, and / or cause the computer 2200 to execute a process according to an embodiment of the present invention or a stage of the process. Such programs may be executed by the CPU 2212 to cause the computer 2200 to perform certain operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

[0065] The computer 2200 according to this embodiment includes a CPU 2212, a RAM 2214, a graphic controller 2216, and a display device 2218, which are interconnected by a host controller 2210. The computer 2200 also includes input / output units such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226, and an IC card drive, which are connected to the host controller 2210 via an input / output controller 2220. The computer also includes legacy input / output units such as a ROM 2230 and a keyboard 2242, which are connected to the input / output controller 2220 via an input / output chip 2240.

[0066] The CPU 2212 operates according to programs stored in the ROM 2230 and the RAM 2214, thereby controlling each unit. The graphic controller 2216 acquires image data generated by the CPU 2212 in a frame buffer provided in the RAM 2214 or the like, or in itself, and causes the image data to be displayed on the display device 2218.

[0067] The communication interface 2222 communicates with other electronic devices via a network. The hard disk drive 2224 stores programs and data used by the CPU 2212 in the computer 2200. The DVD-ROM drive 2226 reads a program or data from the DVD-ROM 2201 and provides the program or data to the hard disk drive 2224 via the RAM 2214. The IC card drive reads programs and data from an IC card and / or writes programs and data to the IC card.

[0068] The ROM 2230 stores therein a boot program or the like executed by the computer 2200 at activation, and / or a program dependent on the hardware of the computer 2200. The input / output chip 2240 may also be connected to the input / output controller 2220 via various input / output units such as a parallel port, a serial port, a keyboard port, a mouse port, etc.

[0069] The program is provided by a computer-readable medium such as a DVD-ROM 2201 or an IC card. The program is read from the computer-readable medium, installed in the hard disk drive 2224, the RAM 2214, or the ROM 2230 which is also an example of a computer-readable medium, and executed by the CPU 2212. The information processing described in these programs is read by the computer 2200, resulting in the cooperation between the programs and the various types of hardware resources described above. The apparatus or method may be configured by realizing the operation or processing of information according to the use of the computer 2200.

[0070] For example, when communication is executed between the computer 2200 and an external device, the CPU 2212 may execute a communication program loaded in the RAM 2214 and instruct the communication interface 2222 to perform communication processing based on the processing described in the communication program. The communication interface 2222 reads the transmission data stored in the transmission buffer processing area provided in a recording medium such as the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or the IC card under the control of the CPU 2212, transmits the read transmission data to the network, or writes the received data received from the network to the reception buffer processing area etc. provided on the recording medium.

[0071] Further, the CPU 2212 may cause all or necessary parts of files or databases stored in external recording media such as a hard disk drive 2224, a DVD-ROM drive 2226 (DVD-ROM 2201), an IC card, etc. to be read into the RAM 2214, and may execute various types of processing on the data on the RAM 2214. The CPU 2212 then writes back the processed data to the external recording media.

[0072] Various types of information such as various types of programs, data, tables, and databases may be stored in the recording media and may be subjected to information processing. The CPU 2212 may execute various types of processing on the data read from the RAM 2214, including various types of operations, information processing, conditional judgment, conditional branch, unconditional branch, information search / replacement, etc. described throughout this disclosure and specified by the instruction sequence of the program, and write back the results to the RAM 2214. Further, the CPU 2212 may search for information in files, databases, etc. within the recording media. For example, when a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording media, the CPU 2212 searches for an entry that matches the condition where the attribute value of the first attribute is specified from among the plurality of entries, reads the attribute value of the second attribute stored in the entry, and thereby may obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0073] The programs or software modules described above may be stored in a computer-readable medium on or near the computer 2200. Also, a recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable medium, thereby providing the program to the computer 2200 via the network.

[0074] As described above, the present invention has been described using embodiments, but the technical scope of the present invention is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various changes or improvements can be made to the above embodiments. It is clear from the description of the claims that forms with such changes or improvements can also be included in the technical scope of the present invention.

[0075] It should be noted that the execution order of each process such as operations, procedures, steps, and stages in the apparatus, system, program, and method shown in the claims, the specification, and the drawings is not explicitly stated as "earlier" or "preceding" etc., and can be realized in any order unless the output of the previous process is used in the subsequent process. Regarding the operation flow in the claims, the specification, and the drawings, even if it is described using "first," "next," etc. for convenience, it does not mean that it is essential to implement in this order.

Description of Reference Numerals

[0076] 1 Device 11 Injection Unit 12 Detection Unit 13 Learning Data Acquisition Unit 14 Storage Unit 15 Learning Processing Unit 16 Supply Unit 17 Identification Unit 100 Biochip 101 Probe 105 Transparent Substrate 110 Nucleic Acid 140 Learning Data File 141 Model 1011 First Probe 1012 Second Probe 1013 Fluorescent Dye 1014 Quencher 2200 Computer 2201 DVD-ROM 2210 Host Controller 2212 CPU 2214 RAM 2216 Graphic Controller 2218 Display Device 2220 Input / Output Controller 2222 Communication Interface 2224 Hard Disk Drive 2226 DVD-ROM Drive 2230 ROM 2240 Input / Output Chip 2242 Keyboard

Claims

1. A detection unit that is provided at a unique position within a biochip and detects the positions of probes among a plurality of probes, at least some of which have different base sequences from each other, that have hybridized with a nucleic acid contained in a sample; A learning processing unit that performs learning processing of a model that outputs the type of organism having the nucleic acid contained in the sample in response to a pattern of positions of probes hybridized with the nucleic acid contained in the sample being newly input, using learning data including the pattern of positions of probes hybridized with the nucleic acid contained in the sample and detected by the detection unit and the type of organism having the nucleic acid contained in the sample; An apparatus comprising the above.

2. The apparatus according to claim 1, wherein the learning processing unit performs separate learning processing of the above models for each type of the biochip in which at least one of the base sequence of the probe or the unique position of the probe is different.

3. A supply unit that supplies a pattern of positions of probes that have hybridized with the nucleic acid contained in one sample and are newly detected by the detection unit to the above model; An identification unit that identifies, as the type of organism having the nucleic acid contained in the one sample, the type of organism output from the model in response to the pattern of positions of probes being supplied by the supply unit; The apparatus according to claim 1, further comprising the above.

4. A detection unit that is provided at a unique position within a biochip and detects the positions of probes among a plurality of probes, at least some of which have different base sequences from each other, that have hybridized with a nucleic acid contained in one sample; A supply unit that supplies a pattern of positions of probes detected by the detection unit to a model that outputs the type of organism having the nucleic acid contained in the sample in response to a pattern of positions of probes hybridized with the nucleic acid contained in the sample being input; An identification unit that identifies, as the type of organism having the nucleic acid contained in the one sample, the type of organism output from the model in response to the pattern of positions of probes being supplied by the supply unit; An apparatus comprising the above.

5. The supply unit supplies, to the model corresponding to the biochip in which the position of the probe has been detected by the detection unit, among the plurality of models that differ for each type of the biochip in which at least one of the base sequence of the probe or the unique position of the probe is different, the pattern of the position of the probe detected by the detection unit. The apparatus according to claim 3.

6. At least a part of the plurality of probes has a base sequence common to a plurality of types of organisms. The apparatus according to claim 1.

7. Each of the plurality of probes has a base sequence with 20 or fewer bases. The apparatus according to claim 1.

8. A detection step of detecting the position of a probe hybridized with a nucleic acid contained in a sample among a plurality of probes each provided at a unique position in a biochip and having at least some probes with different base sequences from each other; A learning process step of performing a learning process of a model that outputs the type of organism having the nucleic acid contained in the sample in response to a new input of a pattern of the position of the probe hybridized with the nucleic acid contained in the sample, using learning data including the pattern of the position of the probe hybridized with the nucleic acid contained in the sample and detected by the detection step and the type of organism having the nucleic acid contained in the sample; A method comprising:

9. A detection step of detecting the position of a probe hybridized with a nucleic acid contained in a sample among a plurality of probes each provided at a unique position in a biochip and having at least some probes with different base sequences from each other; A supply step of supplying, to a model that outputs the type of organism having the nucleic acid contained in the sample in response to an input of a pattern of the position of the probe hybridized with the nucleic acid contained in the sample, the pattern of the position of the probe detected by the detection step; An identification step of identifying, as the type of organism having the nucleic acid contained in the one sample, the type of organism output from the model in response to the supply of the pattern of the position of the probe by the supply step; A method comprising:

10. A computer, A detection unit that detects the position of a probe hybridized with a nucleic acid contained in a sample among a plurality of probes each provided at a unique position in a biochip and having at least some probes with different base sequences from each other; A learning processing unit that performs learning processing of a model that outputs the type of organism having the nucleic acid contained in a sample in response to a pattern of positions of probes hybridized with the nucleic acid contained in the sample being newly input, using learning data including the pattern of positions of the probes detected by the detection unit by hybridizing with the nucleic acid contained in the sample and the type of organism having the nucleic acid contained in the sample. A program for causing the above to function.

11. A computer, A detection unit that is provided at a unique position in a biochip and detects the positions of probes hybridized with the nucleic acid contained in one sample among a plurality of probes each having a different base sequence; A supply unit that supplies the pattern of positions of the probes detected by the detection unit to a model that outputs the type of organism having the nucleic acid contained in the sample in response to a pattern of positions of the probes hybridized with the nucleic acid contained in the sample being input; An identification unit that identifies the type of organism output from the model in response to the pattern of positions of the probes being supplied by the supply unit as the type of organism having the nucleic acid contained in the one sample. A program for causing the above to function.

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