WO2023277230A1 - Electronic device recommending artificial intelligence-based workflow in order to identify glycopeptide, and method for operating same - Google Patents

Abstract

According to various embodiments, an electronic device comprises a processor, wherein the processor may be configured to input, into an artificial intelligence model for recommending a deep neural network (DNN)-based workflow, a mass spectrum of a polypeptide obtained by hydrolyzing a glycoprotein in a sample and a mass spectrum of a glycopeptide to be identified, determine a first mass spectrum having the highest similarity to the mass spectrum of the polypeptide among a plurality of pre-stored first mass spectra by using the workflow-recommending artificial intelligence model, determine a second mass spectrum having the highest similarity to the mass spectrum of the glycopeptide among a plurality of pre-stored second mass spectra related to the first mass spectrum by using the workflow-recommending artificial intelligence model, and use the workflow-recommending artificial intelligence model to recommend a workflow specified by the second mass spectrum and parameter sets to be used in each step of the workflow.

Description

Electronic device for recommending an artificial intelligence-based workflow to identify glycopeptides and its operating method

Various embodiments of the present invention relate to an electronic device that recommends an artificial intelligence-based workflow for identifying glycopeptides and an operating method thereof.

Human blood is a mixture of numerous proteins, of which more than 50% are glycoproteins. However, due to the diversity and complexity of glycoproteins, qualitative or quantitative analysis is difficult compared to proteomic analysis. Recently, with the introduction of high-resolution mass spectrometers, analysis of sugars or glycoproteins is rapidly developing, but bioinformation processing technology capable of identifying and quantifying glycoproteins using the results analyzed in this way is weak.

Glycosylation of proteins is divided into N-linked and O-linked glycosylation. N-linked glycosylation occurs in the endoplasmic reticulum (ER), whereas O-linked glycosylation occurs in the ER, Golgi apparatus or cytosol. O-linked glycosylation is classified into a non-mucin type and a mucin type, and O-linked glycosylation occurring in mammals is mainly of the mucin type. Glycosylation of the mucin type is usually initiated by N-acetylgalactosamine (GalNAc) binding to serine or threonine, and is complex because it occurs directly by an enzyme without the help of a precursor such as dolichol. In addition, O-linked glycoproteins of the mucin type are mainly found in the cytoplasm or nucleus. However, compared to N-linked glycosylation, O-linked glycosylation is relatively less researched and is not yet well known.

Parameters optimized for efficient analysis of O-linked glycopeptides and N-linked glycopeptides, which exhibit relatively low sensitivity, should be applied at the same time. In addition, N-linked and O-linked glycosylation can have various modified forms, and it is difficult to set parameters in advance by applying all modified forms each time. Glycation modified forms include lactylation (72.02Da, C3H4O2), sulfation (79.96Da, SO3), methylation (14.02Da, CH2), phosphorylation (79.97Da, PO3), and O-acetylation (42.01, C2H2O).

In order to identify the mass spectrum of the glycopeptide desired by the user from the mass spectrum of the polypeptide, the user directly adjusting each step and parameter of the workflow consumes a lot of resources in terms of time and cost.

An electronic device and its operating method according to various embodiments of the present invention, in the process of identifying the mass spectrum of a glycopeptide from the mass spectrum of a polypeptide, learns each step and parameter of a workflow input by a user to analyze them later. When a mass spectrum of a polypeptide and/or glycopeptide is input, an optimal workflow and parameter set may be recommended using an artificial intelligence-based workflow recommendation model.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below. You will be able to.

According to various embodiments, the electronic device includes a processor, and the processor converts a mass spectrum of a polypeptide obtained by hydrolyzing a glycoprotein in a sample and a mass spectrum of a glycopeptide to be identified into a DNN (Deep Neural Network)-based workflow recommendation artificial intelligence model, and using the workflow recommendation artificial intelligence model, among a plurality of pre-stored first mass spectra, a first mass spectrum having the highest similarity to the mass spectrum of the polypeptide is obtained. determining a second mass spectrum having the highest similarity to the mass spectrum of the glycopeptide among a plurality of pre-stored second mass spectra associated with the first mass spectrum using the workflow recommendation artificial intelligence model; and a workflow specified by the second mass spectrum and a parameter set to be used in each step of the workflow may be set to be recommended using the workflow recommendation artificial intelligence model.

According to various embodiments, the processor determines the first mass spectrum having the highest similarity with the mass spectrum of the polypeptide or the second mass having the highest similarity with the mass spectrum of the glycopeptide, using the following equation. It can be set to determine the spectrum.

[mathematical expression]

S _i is an (x,y) matrix, where x is the relative intensity of the nth peak, and y is the mass of the nth peak.

_S'i is a (x',y') matrix, where x' is the relative peak intensity of the nth peak, and y' may represent the mass of the nth peak.

According to various embodiments, the processor monitors a process of identifying a mass spectrum of a target glycopeptide from the mass spectrum of a target polypeptide, and determines each of the plurality of first mass spectra in the workflow recommendation artificial intelligence model. It may be set to determine whether the similarity of the mass spectrum of the target polypeptide is equal to or less than a first value for the target polypeptide.

According to various embodiments, the processor may, for all of the plurality of first mass spectra, when the similarity of the mass spectrum of the target polypeptide is equal to or less than a first value: converting the mass spectrum of the target polypeptide into the plurality of first mass spectra Updating the workflow recommendation artificial intelligence model to add a new category of mass spectra, and relating the mass spectrum of the target glycopeptide to the mass spectrum of the target polypeptide, and the specific workflow used in the identification process and the above updating the workflow recommendation artificial intelligence model to associate a set of parameters used in a particular workflow with the mass spectrum of the target glycopeptide, and for at least a portion of the first plurality of mass spectra, the mass spectrum of the target polypeptide; When the similarity of exceeds the first value: for each of a plurality of third mass spectra associated with a mass spectrum having the highest similarity with the mass spectrum of the target polypeptide among the at least some first mass spectra, the target polypeptide It may be set to determine whether the similarity of the mass spectrum of the glycopeptide is equal to or less than the second value.

According to various embodiments, the processor may, for all of the plurality of third mass spectra, when the similarity of the mass spectrum of the target glycopeptide is equal to or less than the second value: converting the mass spectrum of the target glycopeptide into the plurality of mass spectra Updating the workflow recommendation artificial intelligence model to add a new category of third mass spectra of , and the specific workflow used in the identification process and the parameter set used in the specific workflow as the target glycopeptide It can be set to update the workflow recommendation artificial intelligence model to associate with the mass spectrum of .

According to various embodiments, the processor may, with respect to at least a portion of the plurality of third mass spectra, when a similarity of mass spectra of the target glycopeptide exceeds the second value, the at least a portion of the third mass spectra The mass spectrum of the target glycopeptide having the highest similarity with the mass spectrum of the target glycopeptide is determined among the spectra, and the mass spectrum of the target glycopeptide excluding the mass spectrum of the target glycopeptide is determined from the plurality of third mass spectra. A first average similarity of the mass spectrum of the glycopeptide and a second average similarity of the mass spectrum of the glycopeptide to be compared are calculated, the first average similarity and the second average similarity are compared, and the first average similarity is If it is lower than the second average similarity, the mass spectrum of the glycopeptide to be compared included in the plurality of third mass spectra is replaced with the mass spectrum of the glycopeptide to be compared, and the mass spectrum of the glycopeptide to be compared is specified. The workflow recommendation artificial intelligence model is updated to replace the workflow and parameter set used to identify the mass spectrum of the target glycopeptide, and the first average similarity is the second average similarity. If higher than the average similarity, the mass spectrum of the glycopeptide to be compared included in the plurality of third mass spectra is maintained, and the workflow and the parameter set specified by the mass spectrum of the glycopeptide to be compared are maintained. It may be set to process the workflow recommendation artificial intelligence model to do so.

According to various embodiments of the present invention, in order to efficiently and accurately identify glycopeptides present with relatively low sensitivity (or concentration) compared to general peptides using the mass spectrum results obtained from the latest high-resolution mass spectrometer, artificial intelligence-based By providing a method for recommending a workflow and a parameter set to be used in the workflow, it is possible to provide a method for easily analyzing mass spectra without being an expert in mass spectrometry.

The effects obtainable in the present invention are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the description below. will be.

Also other aspects as described above, features and benefits of certain preferred embodiments of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings.

1 shows a block diagram of an electronic device and network according to various embodiments of the present invention.

2 is a block diagram of an electronic device according to various embodiments.

3 is a block diagram of program modules according to various embodiments.

4 is a flowchart illustrating a method of recommending an artificial intelligence-based workflow by an electronic device according to various embodiments.

5 shows a mass spectrum of a polypeptide learned and classified by a workflow recommendation artificial intelligence model, a mass spectrum of a glycopeptide, an optimal workflow for identifying the glycopeptide, and use in the workflow, according to various embodiments. A table showing the relationship of optimal parameter sets to be

6 shows an example of spectral similarity, in accordance with various embodiments.

7A-7C show example workflows, according to various embodiments.

8 shows an example of a parameter set, according to various embodiments.

9 is a flowchart illustrating a method for an electronic device to learn a workflow recommendation artificial intelligence model, according to various embodiments.

10 shows a mass spectrum of a polypeptide added to the table of FIG. 5 by an artificial intelligence model recommending a workflow, a mass spectrum of a glycopeptide, an optimal workflow for identifying the glycopeptide, and the above A table showing the relationship of optimal parameter sets used in the workflow is shown.

It should be noted that throughout the drawings, like reference numbers are used to show the same or similar elements, features and structures.

Hereinafter, various embodiments of this document will be described with reference to the accompanying drawings. Examples and terms used therein are not intended to limit the technology described in this document to specific embodiments, and should be understood to include various modifications, equivalents, and/or substitutes of the embodiments. In connection with the description of the drawings, like reference numerals may be used for like elements. Singular expressions may include plural expressions unless the context clearly dictates otherwise. In this document, expressions such as "A or B" or "at least one of A and/or B" may include all possible combinations of the items listed together. Expressions such as "first," "second," "first," or "second," may modify the corresponding components regardless of order or importance, and are used to distinguish one component from another. It is used only and does not limit the corresponding components. When a (e.g., first) element is referred to as being "(functionally or communicatively) coupled to" or "connected to" another (e.g., second) element, that element refers to the other (e.g., second) element. It may be directly connected to the component or connected through another component (eg, a third component).

In this document, "configured (or configured to)" means "suitable for," "having the ability to," "changed to," depending on the situation, for example, hardware or software. ," can be used interchangeably with "made to," "capable of," or "designed to." In some contexts, the expression "device configured to" can mean that the device is "capable of" in conjunction with other devices or components. For example, the phrase "a processor configured (or configured) to perform A, B, and C" may include a dedicated processor (eg, embedded processor) to perform the operation, or by executing one or more software programs stored in a memory device. , may mean a general-purpose processor (eg, CPU or application processor) capable of performing corresponding operations.

Electronic devices according to various embodiments of the present document include, for example, a smart phone, a tablet PC, a mobile phone, a video phone, an e-book reader, a desktop PC, a laptop PC, a netbook computer, a workstation, a server, a PDA, and a PMP. It may include at least one of a portable multimedia player, an MP3 player, a medical device, a camera, or a wearable device. A wearable device may be in the form of an accessory (e.g. watch, ring, bracelet, anklet, necklace, eyeglasses, contact lens, or head-mounted-device (HMD)), integrated into textiles or clothing (e.g. electronic garment); In some embodiments, the electronic device may include, for example, a television, a digital video disk (DVD) player, Audio, refrigerator, air conditioner, vacuum cleaner, oven, microwave, washing machine, air purifier, set top box, home automation control panel, security control panel, media box (e.g. Samsung HomeSync ^TM , Apple TV ^TM , or Google TV ^TM ) , a game console (eg, Xbox ^TM , PlayStation ^TM ), an electronic dictionary, an electronic key, a camcorder, or an electronic picture frame.

In another embodiment, the electronic device may include various types of medical devices (e.g., various portable medical measuring devices (such as blood glucose meter, heart rate monitor, blood pressure monitor, or body temperature monitor), magnetic resonance angiography (MRA), magnetic resonance imaging (MRI), CT (computed tomography), imager, or ultrasonicator, etc.), navigation device, global navigation satellite system (GNSS), EDR (event data recorder), FDR (flight data recorder), automobile infotainment device, marine electronic equipment (e.g. navigation devices for ships, gyrocompasses, etc.), avionics, security devices, head units for vehicles, industrial or home robots, drones, ATMs in financial institutions, point of sale (POS) in stores of sales), or IoT devices (eg, light bulbs, various sensors, sprinkler devices, fire alarms, thermostats, street lights, toasters, exercise equipment, hot water tanks, heaters, boilers, etc.). According to some embodiments, the electronic device may be a piece of furniture, a building/structure or a vehicle, an electronic board, an electronic signature receiving device, a projector, or various measuring devices (eg, water, electricity, gas, radio wave measuring device, etc.). In various embodiments, the electronic device may be flexible or a combination of two or more of the various devices described above. An electronic device according to an embodiment of the present document is not limited to the aforementioned devices. In this document, the term user may refer to a person using an electronic device or a device using an electronic device (eg, an artificial intelligence electronic device).

Referring to FIG. 1 , an electronic device 101 in a network environment 100 in various embodiments is described. The electronic device 101 may include a bus 110, a processor 120, a memory 130, an input/output interface 150, a display 160, and a communication interface 170. Hereinafter, the electronic device 101 according to an embodiment of the present invention will be described with reference to FIGS. 1 to 3 .

4 is a flowchart illustrating a method of recommending an artificial intelligence-based workflow by an electronic device (eg, the electronic device 101 of FIG. 1 ) according to various embodiments.

5 shows a mass spectrum of a polypeptide learned and classified by a workflow recommendation artificial intelligence model, a mass spectrum of a glycopeptide, an optimal workflow for identifying the glycopeptide, and use in the workflow, according to various embodiments. A table showing the relationship of optimal parameter sets to be

6 shows an example of spectral similarity, in accordance with various embodiments.

7A-7C show example workflows, according to various embodiments.

8 shows an example of a parameter set, according to various embodiments.

In operation S410, according to various embodiments, the electronic device 101 (eg, the processor 120 of FIG. 1) hydrolyzes the glycoprotein in the sample to obtain the mass spectrum of the polypeptide and the sugar to be identified. The mass spectrum of a peptide can be input into a workflow recommendation artificial intelligence model based on a Deep Neural Network (DNN). For example, in order to analyze the mass spectrum of a specific glycopeptide from the mass spectrum of a specific polypeptide, the user may input the mass spectrum of the specific polypeptide and the mass spectrum of the specific glycopeptide into a DNN-based workflow recommendation artificial intelligence model. can

According to one embodiment, “hydrolysis of glycoproteins” refers to a process of separating only sugars from glycoproteins. The hydrolysis may be performed using any method well known in the art. In particular, the hydrolysis may be performed using a hydrolase, which is specifically, trypsin, arginine C (Arg-C), aspartic acid N (Asp-N), glutamic acid C (Glu-C) , Lys-C, chymotrypsin, and proteinase K.

According to one embodiment, a mass spectrum of a polypeptide can be obtained by analyzing the polypeptide with a high resolution mass spectrometer. Specifically, a mass spectrometer can be used to efficiently qualitatively and quantitatively analyze glycopeptides (eg, O-linked glycopeptides) that are complex, highly diverse, and present in a low concentration in a sample compared to general peptides. Glycopeptides may be identified using M-score, S-score, Y-score, and P-score based on the results obtained from the mass spectrometer, and quantitative analysis of the identified glycopeptide may be performed.

The mass spectrometer may have a mass resolution of 10,000 or more and a mass accuracy of 50 ppm or less. According to one embodiment, the mass spectrometer may be an Orbitrap ^™ mass spectrometer and/or a Q Exactive ^™ mass spectrometer.

According to an embodiment, the DNN-based workflow recommendation artificial intelligence model is classified in a library for each process so as to be assembled by process with respect to a data pre-processing process, a learning/classification process, a data post-processing process, and an iterative learning/classification process. can be stored According to one embodiment, the workflow recommendation artificial intelligence model learns ⓐ each mass spectrum of polypeptides, ⓑ each mass spectrum of glycopeptides, ⓒ each step of the workflow selected by the user, and ⓓ parameter sets selected by the user. By doing so, it is possible to recommend a workflow suitable for extracting the mass spectrum of a glycopeptide to be identified from the mass spectrum of a polypeptide input later and a set of parameters to be input in each step of the workflow to the user.

In operation S420, according to various embodiments, the electronic device 101 (eg, the processor 120 of FIG. 1 ) selects among a plurality of pre-stored first mass spectra using a DNN-based workflow recommendation artificial intelligence model. A first mass spectrum having the highest similarity to the mass spectrum of the input polypeptide may be determined. This is to recommend to the user an analysis environment for a mass spectrum of a polypeptide most similar to the mass spectrum of an input polypeptide among mass spectra of polypeptides for which mass spectrometry has already been completed.

According to an embodiment, the electronic device 101 may store a plurality of first mass spectra of mass spectra of polypeptides in a memory (eg, the memory 130 of FIG. 1 ). For example, referring to FIG. 5 , the electronic device 101 stores in the memory 130 a mass spectrum (eg, A1) of a first polypeptide obtained by hydrolyzing a glycoprotein in a first sample and a glycoprotein in a second sample. Data on the mass spectrum (eg, A2) of a second polypeptide obtained by hydrolyzing a protein may be stored, and among a plurality of first mass spectra (eg, A1, A2), similarity to the mass spectrum of the input polypeptide The first mass spectrum (eg, A1) with the highest A may be determined.

According to an embodiment, the mass spectrum similarity (SS, spectral similarity) may be calculated by Equation 1 below.

[Equation 1]

Si: (x,y) matrix, where x is the nth relative peak intensity, y is the mass of the nth peak, and

S'i: (x',y') matrix, where x' is the relative peak intensity of the nth peak, y' is the mass of the nth peak

In operation S430, according to various embodiments, the electronic device 101 (eg, the processor 120 of FIG. 1) uses a DNN-based workflow recommendation artificial intelligence model to pre-store the first mass spectrum associated with the first mass spectrum. Among the plurality of second mass spectra, a second mass spectrum having the highest similarity to the mass spectrum of the input glycopeptide may be determined. This is to recommend a glycopeptide mass spectrum analysis environment most similar to the input glycopeptide mass spectrum among mass spectra of glycopeptides for which mass analysis has already been completed, to the user.

According to an embodiment, the electronic device 101 may store a plurality of second mass spectra of glycopeptides in the memory 130 . For example, referring to FIG. 5 , the electronic device 101 includes mass spectra (eg, B1, B2, B3, Data on the mass spectra (eg C1, C2, C3, etc.) of the glycopeptide identified from the mass spectrum (eg A2) of the second polypeptide and the mass spectrum of the second polypeptide can be stored, and the similarity to the mass spectrum of the input polypeptide Among a plurality of second mass spectra (eg, B1, B2, B3, 쪋) associated with the first mass spectrum (eg, A1) with the highest, the second mass having the highest similarity to the mass spectrum of the input glycopeptide Spectrum (e.g. B1) can be determined. According to one embodiment, a method for determining the second mass spectrum having the highest similarity with the mass spectrum of the input glycopeptide may use [Equation 1] of operation S420. For example, referring to FIG. 6, the electronic device uses [Equation 1], and the mass spectrum (eg, TPLPPT~_(2HexNAc-2Hex)) and the similarity (eg, 0.97) of the input glycopeptide are the most A high second mass spectrum (eg, TPLPPT~_(2HexNAc-2Hex-NeuAc)) can be determined.

In operation S440, according to various embodiments, the electronic device 101 (eg, the processor 120 of FIG. 1) uses a DNN-based workflow recommendation artificial intelligence model to perform the work specified by the second mass spectrum. A flow and parameters to be used in each step of the workflow may be recommended to the user.

According to one embodiment, the electronic device 101 may store a workflow corresponding to the mass spectrum of each glycopeptide in the memory 130 . For example, referring to FIG. 5 , the electronic device 101 stores mass spectra (eg, B1, B2, B3) of glycopeptides identified from the mass spectrum (eg, A1) of the first polypeptide in the memory 130. Workflows (eg, D1, D2, D3) corresponding to may be stored, and mass spectra (eg, C1, C2, C3) of glycopeptides identified from the mass spectrum (eg, A2) of the second polypeptide Corresponding workflows (eg, D4, D5, D6) can be saved.

For example, referring to FIG. 7 , each workflow may mean an optimal workflow for deriving a mass spectrum of a corresponding glycopeptide. According to one embodiment, the electronic device 101 may store in the memory 130 parameter sets to be used in each step of the workflow corresponding to the mass spectrum of each glycopeptide. For example, referring to FIG. 5 , the electronic device 101 stores mass spectra (eg, B1, B2, B3) of glycopeptides identified from the mass spectrum (eg, A1) of the first polypeptide in the memory 130. Parameter sets (eg, E1, E2, E3) to be used in each step of workflows (eg, D1, D2, D3) corresponding to may be stored, and identified from the mass spectrum (eg, A2) of the second polypeptide. Parameter sets (eg, E4, E5, E6) to be used in each step of workflows (eg, D4, D5, D6) corresponding to mass spectra (eg, C1, C2, C3) of glycopeptides may be stored. .

For example, referring to FIG. 8 , each parameter set may mean optimal parameters for deriving a mass spectrum of a corresponding glycopeptide. According to an embodiment, the electronic device 101 may recommend a workflow specified by the second mass spectrum and a parameter set to be used in each step of the workflow within a DNN-based workflow recommendation artificial intelligence model to the user. can For example, referring to FIG. 5 , the electronic device 101 displays a first mass spectrum (eg, A1) having the highest similarity to the mass spectrum of the input polypeptide and a mass spectrum having the highest similarity to the mass spectrum of the input glycopeptide. A first workflow (eg, D1) specified by the second mass spectrum (eg, B1) and a parameter set (eg, E1) to be used in each step of the first workflow may be recommended to the user. According to an embodiment, the operation of recommending the workflow and the parameter set includes an operation of displaying the workflow and the parameter set on a display (eg, the display 160 of FIG. 1 ) or using the workflow and the parameter set. and analyzing the mass spectrum of the input polypeptide.

9 is a flowchart illustrating a method for an electronic device (eg, the electronic device 101 of FIG. 1 ) to learn a workflow recommendation artificial intelligence model according to various embodiments.

10 shows a mass spectrum of a polypeptide added to the table of FIG. 5 by an artificial intelligence model recommending a workflow, a mass spectrum of a glycopeptide, an optimal workflow for identifying the glycopeptide, and the above A table showing the relationship of optimal parameter sets used in the workflow is shown.

In operation S901, according to various embodiments, the electronic device 101 (eg, the processor 120 of FIG. 1) determines the mass spectrum of the target glycopeptide from the mass spectrum of the target polypeptide obtained by hydrolyzing the glycoprotein in the sample. The identification process can be monitored. For example, the electronic device 101 may monitor a process of identifying the mass spectrum of a target glycopeptide by inputting specific parameters according to each step of a specific workflow.

In operation S903, according to various embodiments, the electronic device 101 (eg, the processor 120 of FIG. 1 ) selects a plurality of first mass spectra within a deep neural network (DNN)-based workflow recommendation artificial intelligence model. For each, it can be determined whether the similarity of the mass spectrum of the target polypeptide is equal to or less than a first value.

For example, referring to FIG. 10 , the electronic device 101 may determine similarity with the mass spectrum of the target polypeptide for each of a plurality of first mass spectra (eg, A1 and A2), and determine a plurality of first mass spectra (eg, A1 and A2). Determining whether the similarity of the mass spectra of the target polypeptide is less than or equal to a first value for all of the first mass spectra, or whether the similarity of the mass spectra of the target polypeptide exceeds the first value for at least a portion of the plurality of first mass spectra. can do. According to an embodiment, the electronic device may determine similarity between the plurality of first mass spectra and the mass spectrum of the target polypeptide using [Equation 1] of operation S420.

In operation S905, according to various embodiments, the electronic device 101 (eg, the processor 120 of FIG. 1) sets the similarity of the mass spectrum of the target polypeptide to the first value with respect to all of the plurality of first mass spectra. In the case below, the workflow recommendation artificial intelligence model may be updated to add the mass spectrum of the target polypeptide to a new category of the plurality of first mass spectra.

For example, referring to FIG. 10 , the electronic device 101 updates the workflow recommendation artificial intelligence model to add the mass spectrum of the target polypeptide to a new category (eg, A3) of the plurality of first mass spectra. can In this case, the electronic device 101 associates the mass spectrum (eg, G1) of the target glycopeptide identified from the mass spectrum (eg, A3) of the target polypeptide with the mass spectrum of the target polypeptide, and in the identification process The workflow recommendation artificial intelligence model can be updated to relate the workflow used (eg H1) and the parameter set used in the workflow (eg I1) to the mass spectrum of the target glycopeptide.

In operation S907, according to various embodiments, the electronic device 101 (eg, the processor 120 of FIG. 1) determines the similarity of the mass spectrum of the target polypeptide with respect to at least a portion of the plurality of first mass spectra. When the value exceeds 1, the mass spectrum of the target glycopeptide for each of the plurality of second mass spectra associated with the mass spectrum having the highest similarity with the mass spectrum of the target polypeptide among the at least part of the first mass spectrum It may be determined whether the degree of similarity is equal to or less than the second value.

For example, referring to FIG. 10 , the electronic device 101 provides a plurality of second mass spectra (eg, B1) associated with a mass spectrum (eg, A1) of a polypeptide having the highest similarity to the mass spectrum of the target polypeptide. , B2, B3), it is possible to determine the degree of similarity with the mass spectrum of the target glycopeptide, and for all of the plurality of second mass spectra, whether the similarity of the mass spectrum of the target glycopeptide is equal to or less than a second value Alternatively, with respect to at least a portion of the plurality of second mass spectra, it may be determined whether the similarity of the mass spectrum of the target glycopeptide exceeds the second value. According to one embodiment, the electronic device may determine similarity between the plurality of second mass spectra and the mass spectrum of the target glycopeptide using [Equation 1] of operation S420.

In operation S909, according to various embodiments, the electronic device 101 (eg, the processor 120 of FIG. 1 ) determines the similarity of the mass spectrum of the target glycopeptide to the second mass spectrum with respect to all of the plurality of second mass spectra. value, the workflow recommendation artificial intelligence model may be updated to add the mass spectrum of the target glycopeptide to a new category of the plurality of second mass spectra.

For example, referring to FIG. 10 , the electronic device 101 updates the workflow recommendation artificial intelligence model to add the mass spectrum of the target glycopeptide to a new category (eg, B4) of the plurality of second mass spectra. can do. In this case, the electronic device 101 converts the workflow (eg, D7) used in the process of identifying the target glycopeptide and the parameter set (eg, E7) used in the workflow to the mass spectrum of the target glycopeptide. The workflow recommendation artificial intelligence model may be updated to be relevant.

In operation S911, according to various embodiments, the electronic device 101 (eg, the processor 120 of FIG. 1) determines the similarity of the mass spectrum of the target glycopeptide with respect to at least a portion of the plurality of second mass spectra. When the second value is exceeded, the mass spectrum of the comparison target glycopeptide having the highest similarity to the mass spectrum of the target glycopeptide among the at least part of the second mass spectrum may be determined.

For example, referring to FIG. 10 , the electronic device 101 determines the concentration of the target glycopeptide with respect to at least a portion (eg, B2, B3) of a plurality of second mass spectra (eg, B1, B2, and B3). When the similarity of the mass spectrum exceeds the second value, the mass spectrum of the target glycopeptide having the highest similarity to the mass spectrum of the target glycopeptide among the at least part of the second mass spectrum (eg, B2, B3) (eg, B2, B3) : B2) can be judged.

In operation S913, according to various embodiments, the electronic device 101 (eg, the processor 120 of FIG. 1) determines the remaining mass spectrum except for the mass spectrum of the glycopeptide to be compared in the plurality of second mass spectra. A first average similarity of the mass spectrum of the target glycopeptide and a second average similarity of the mass spectrum of the comparison target glycopeptide may be calculated, and the first average similarity and the second average similarity may be compared.

For example, referring to FIG. 10 , the electronic device 101 excludes the mass spectrum of the glycopeptide to be compared (eg, B2) from the plurality of second mass spectra (eg, B1, B2, and B3) and the rest of the mass spectrum. (Example: B1, B3) Calculate the average value of the similarity of the mass spectrum of the target glycopeptide (i.e., the first average similarity) for each of the mass spectra (eg, B1, B3), and compare the above for each of the remaining mass spectra (eg B1, B3) An average value (ie, a second average similarity) of similarities of the mass spectrum (eg, B2) of the target glycopeptide may be calculated, and the first average similarity and the second average similarity may be compared.

According to one embodiment, the electronic device uses [Equation 1] in operation S420 to use the mass spectrum of the target glycopeptide for each of the mass spectra excluding the mass spectrum of the glycopeptide to be compared in the plurality of second mass spectra. The similarity of and the similarity of the mass spectrum of the glycopeptide to be compared can be calculated.

In operation S915, according to various embodiments, when the first average similarity is lower than the second average similarity, the electronic device 101 (eg, the processor 120 of FIG. 1) selects a plurality of second mass spectra. In order to replace the mass spectrum of the glycopeptide to be compared with the mass spectrum of the glycopeptide to be compared with the mass spectrum of the glycopeptide to be compared, and to identify the mass spectrum of the glycopeptide with the workflow and parameter set specified by the mass spectrum of the glycopeptide to be compared The workflow recommendation AI model can be updated to replace the used workflow and parameter set.

For example, referring to FIG. 10 , when the first average similarity is lower than the second average similarity, the electronic device 101 includes a plurality of second mass spectra (eg, B1, B2, and B3). The mass spectrum (eg B2) of the glycopeptide to be compared is replaced with the mass spectrum (eg B5) of the glycopeptide to be compared, and the workflow specified by the mass spectrum (eg B2) of the glycopeptide to be compared (eg B2) is performed. D2) and parameter set (eg E2) to the workflow (eg D8) and parameter set (eg E8) used to identify the mass spectrum (eg B5) of the target glycopeptide. The flow recommendation AI model can be updated.

In operation S917, according to various embodiments, when the first average similarity is higher than the second average similarity, the electronic device 101 (eg, the processor 120 of FIG. 1) selects a plurality of second mass spectra. The workflow recommendation artificial intelligence model may be processed to maintain the mass spectrum of the glycopeptide to be compared included in , and to maintain the workflow and parameter set specified by the mass spectrum of the glycopeptide to be compared.

For example, referring to FIG. 10 , when the first average similarity is higher than the second average similarity, the electronic device 101 includes a plurality of second mass spectra (eg, B1, B2, and B3). The mass spectrum (eg B2) of the glycopeptide to be compared is maintained, and the workflow (eg D2) and parameter set (eg E2) specified by the mass spectrum (eg B2) of the glycopeptide to be compared are maintained. The workflow recommendation artificial intelligence model may be maintained without updating.

According to various embodiments, the electronic device includes a processor, and the processor converts a mass spectrum of a polypeptide obtained by hydrolyzing a glycoprotein in a sample and a mass spectrum of a glycopeptide to be identified into a DNN (Deep Neural Network)-based workflow recommendation artificial intelligence model, and using the workflow recommendation artificial intelligence model, among a plurality of pre-stored first mass spectra, a first mass spectrum having the highest similarity to the mass spectrum of the polypeptide is obtained. determining a second mass spectrum having the highest similarity to the mass spectrum of the glycopeptide among a plurality of pre-stored second mass spectra associated with the first mass spectrum using the workflow recommendation artificial intelligence model; and a workflow specified by the second mass spectrum and a parameter set to be used in each step of the workflow may be set to be recommended using the workflow recommendation artificial intelligence model.

According to various embodiments, the processor determines the first mass spectrum having the highest similarity to the mass spectrum of the polypeptide or the second mass spectrum having the highest similarity to the mass spectrum of the glycopeptide, using Equation 1 below. It can be set to determine the mass spectrum.

S _i : (x,y) matrix, where x is the relative peak intensity of the nth peak, and y is the mass of the nth peak

S' _i : (x',y') matrix, where x' is the relative peak intensity of the nth peak, and y' is the mass of the nth peak

According to various embodiments, the processor monitors a process of identifying a mass spectrum of a target glycopeptide from the mass spectrum of a target polypeptide, and determines each of the plurality of first mass spectra in the workflow recommendation artificial intelligence model. It may be set to determine whether the similarity of the mass spectrum of the target polypeptide is equal to or less than a first value for the target polypeptide.

According to various embodiments, the processor may, for all of the plurality of first mass spectra, when the similarity of the mass spectrum of the target polypeptide is equal to or less than a first value: converting the mass spectrum of the target polypeptide into the plurality of first mass spectra Updating the workflow recommendation artificial intelligence model to add a new category of mass spectra, and relating the mass spectrum of the target glycopeptide to the mass spectrum of the target polypeptide, and the specific workflow used in the identification process and the above updating the workflow recommendation artificial intelligence model to associate a set of parameters used in a particular workflow with the mass spectrum of the target glycopeptide, and for at least a portion of the first plurality of mass spectra, the mass spectrum of the target polypeptide; When the similarity of exceeds the first value: for each of a plurality of third mass spectra associated with a mass spectrum having the highest similarity with the mass spectrum of the target polypeptide among the at least some first mass spectra, the target polypeptide It may be set to determine whether the similarity of the mass spectrum of the glycopeptide is equal to or less than the second value.

According to various embodiments, the processor may, for all of the plurality of third mass spectra, when the similarity of the mass spectrum of the target glycopeptide is equal to or less than the second value: converting the mass spectrum of the target glycopeptide into the plurality of mass spectra Updating the workflow recommendation artificial intelligence model to add a new category of third mass spectra of , and the specific workflow used in the identification process and the parameter set used in the specific workflow as the target glycopeptide It can be set to update the workflow recommendation artificial intelligence model to associate with the mass spectrum of .

According to various embodiments, the processor may, with respect to at least a portion of the plurality of third mass spectra, when a similarity of mass spectra of the target glycopeptide exceeds the second value, the at least a portion of the third mass spectra The mass spectrum of the target glycopeptide having the highest similarity with the mass spectrum of the target glycopeptide is determined among the spectra, and the mass spectrum of the target glycopeptide excluding the mass spectrum of the target glycopeptide is determined from the plurality of third mass spectra. A first average similarity of the mass spectrum of the glycopeptide and a second average similarity of the mass spectrum of the glycopeptide to be compared are calculated, the first average similarity and the second average similarity are compared, and the first average similarity is If it is lower than the second average similarity, the mass spectrum of the glycopeptide to be compared included in the plurality of third mass spectra is replaced with the mass spectrum of the glycopeptide to be compared, and the mass spectrum of the glycopeptide to be compared is specified. The workflow recommendation artificial intelligence model is updated to replace the workflow and parameter set used to identify the mass spectrum of the target glycopeptide, and the first average similarity is the second average similarity. If higher than the average similarity, the mass spectrum of the glycopeptide to be compared included in the plurality of third mass spectra is maintained, and the workflow and the parameter set specified by the mass spectrum of the glycopeptide to be compared are maintained. It may be set to process the workflow recommendation artificial intelligence model to do so.

In some embodiments, the electronic device 101 may omit at least one of the components or may additionally include other components. Bus 110 may include circuitry that connects components 110-170 to each other and communicates (eg, control messages or data) between components. The processor 120 may include one or more of a central processing unit, an application processor, or a communication processor (CP). The processor 120 may, for example, execute calculations or data processing related to control and/or communication of at least one other component of the electronic device 101 .

Memory 130 may include volatile and/or non-volatile memory. The memory 130 may store, for example, commands or data related to at least one other component of the electronic device 101 . According to one embodiment, memory 130 may store software and/or programs 140 . The program 140 may include, for example, a kernel 141, middleware 143, an application programming interface (API) 145, and/or an application program (or “application”) 147, and the like. . At least part of the kernel 141, middleware 143, or API 145 may be referred to as an operating system. Kernel 141, for example, includes system resources (eg, middleware 143, API 145, or application program 147) used to execute operations or functions implemented in other programs (eg, middleware 143, API 145, or application program 147). : The bus 110, the processor 120, or the memory 130, etc.) can be controlled or managed. In addition, the kernel 141 may provide an interface capable of controlling or managing system resources by accessing individual components of the electronic device 101 from the middleware 143, API 145, or application program 147. can

The middleware 143 may perform an intermediary role so that, for example, the API 145 or the application program 147 communicates with the kernel 141 to exchange data. Also, the middleware 143 may process one or more task requests received from the application program 147 according to priority. For example, the middleware 143 may use system resources (eg, the bus 110, the processor 120, or the memory 130, etc.) of the electronic device 101 for at least one of the application programs 147. Prioritize and process the one or more work requests. The API 145 is an interface for the application 147 to control functions provided by the kernel 141 or the middleware 143, for example, at least for file control, window control, image processing, or text control. It can contain one interface or function (eg command). The input/output interface 150 transmits, for example, a command or data input from a user or other external device to other component(s) of the electronic device 101, or other components of the electronic device 101 ( s) may output commands or data received from the user or other external devices.

The display 160 may be, for example, a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a microelectromechanical system (MEMS) display, or an electronic paper display. can include The display 160 may display various types of content (eg, text, image, video, icon, and/or symbol) to the user. The display 160 may include a touch screen, and may receive, for example, a touch, gesture, proximity, or hovering input using an electronic pen or a part of the user's body. The communication interface 170 establishes communication between the electronic device 101 and an external device (eg, the first external electronic device 102 , the second external electronic device 104 , or the server 106 ). can For example, the communication interface 170 may be connected to the network 162 through wireless or wired communication to communicate with an external device (eg, the second external electronic device 104 or the server 106).

Wireless communication is, for example, LTE, LTE-A (LTE Advance), CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband), or GSM (Global System for Mobile Communications) may include cellular communication using at least one of the like. According to one embodiment, wireless communication, for example, WiFi (wireless fidelity), Bluetooth, Bluetooth Low Energy (BLE), Zigbee, near field communication (NFC), magnetic secure transmission (Magnetic Secure Transmission), radio It may include at least one of a frequency (RF) and a body area network (BAN). According to one embodiment, wireless communication may include GNSS. The GNSS may be, for example, a Global Positioning System (GPS), a Global Navigation Satellite System (Glonass), a Beidou Navigation Satellite System (hereinafter “Beidou”) or Galileo, the European global satellite-based navigation system. Hereinafter, in this document, "GPS" may be used interchangeably with "GNSS". Wired communication may include, for example, at least one of universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), power line communication, or plain old telephone service (POTS). there is. Network 162 may include at least one of a telecommunication network, for example, a computer network (eg, LAN or WAN), the Internet, or a telephone network.

Each of the first and second external electronic devices 102 and 104 may be the same as or different from the electronic device 101 . According to various embodiments, all or part of operations executed in the electronic device 101 may be executed in one or more electronic devices (eg, the electronic devices 102 and 104, or the server 106). According to this, when the electronic device 101 needs to perform a certain function or service automatically or upon request, the electronic device 101 instead of or in addition to executing the function or service by itself, at least some functions related thereto may request another device (eg, the electronic device 102 or 104 or the server 106). The additional function may be executed and the result may be transmitted to the electronic device 101. The electronic device 101 may provide the requested function or service by processing the received result as it is or additionally. For example, cloud computing, distributed computing, or client-server computing technologies may be used.

2 is a block diagram of an electronic device 201 according to various embodiments. The electronic device 201 may include, for example, all or part of the electronic device 101 shown in FIG. 1 . The electronic device 201 includes one or more processors (eg, APs) 210, a communication module 220, a subscriber identification module 224, a memory 230, a sensor module 240, an input device 250, a display 260, interface 270, audio module 280, camera module 291, power management module 295, battery 296, indicator 297, and motor 298. Processor The processor 210 may control a plurality of hardware or software components connected to the processor 210 by driving, for example, an operating system or an application program, and may perform various data processing and calculations. ) may be implemented as, for example, a system on chip (SoC) According to one embodiment, the processor 210 may further include a graphic processing unit (GPU) and/or an image signal processor. 210 may include at least some (eg, cellular module 221) of the components shown in Fig. 2. The processor 210 may include at least one of the other components (eg, non-volatile memory). Received commands or data may be loaded into volatile memory for processing, and resultant data may be stored in non-volatile memory.

It may have the same or similar configuration as the communication module 220 (eg, the communication interface 170). The communication module 220 may include, for example, a cellular module 221, a WiFi module 223, a Bluetooth module 225, a GNSS module 227, an NFC module 228 and an RF module 229. there is. The cellular module 221 may provide, for example, a voice call, a video call, a text service, or an Internet service through a communication network. According to one embodiment, the cellular module 221 may identify and authenticate the electronic device 201 within a communication network using the subscriber identity module (eg, SIM card) 224 . According to one embodiment, the cellular module 221 may perform at least some of the functions that the processor 210 may provide. According to one embodiment, the cellular module 221 may include a communication processor (CP). According to some embodiments, at least some (eg, two or more) of the cellular module 221, the WiFi module 223, the Bluetooth module 225, the GNSS module 227, or the NFC module 228 are one integrated chip (IC) or within an IC package. The RF module 229 may transmit and receive communication signals (eg, RF signals), for example. The RF module 229 may include, for example, a transceiver, a power amp module (PAM), a frequency filter, a low noise amplifier (LNA), or an antenna. According to another embodiment, at least one of the cellular module 221, the WiFi module 223, the Bluetooth module 225, the GNSS module 227, or the NFC module 228 transmits and receives an RF signal through a separate RF module. can The subscriber identification module 224 may include, for example, a card or an embedded SIM including a subscriber identification module, and may include unique identification information (eg, integrated circuit card identifier (ICCID)) or subscriber information (eg, IMSI). (international mobile subscriber identity)).

The memory 230 (eg, the memory 130) may include, for example, an internal memory 232 or an external memory 234. The built-in memory 232 may include, for example, volatile memory (eg, DRAM, SRAM, SDRAM, etc.), non-volatile memory (eg, OTPROM (one time programmable ROM), PROM, EPROM, EEPROM, mask ROM, flash ROM). , a flash memory, a hard drive, or a solid state drive (SSD).The external memory 234 may include a flash drive, for example, a compact flash (CF) or secure digital (SD). ), Micro-SD, Mini-SD, extreme digital (xD), multi-media card (MMC), or memory stick, etc. The external memory 234 is functionally compatible with the electronic device 201 through various interfaces. can be physically or physically connected.

The sensor module 240 may, for example, measure a physical quantity or detect an operating state of the electronic device 201 and convert the measured or sensed information into an electrical signal. The sensor module 240 includes, for example, a gesture sensor 240A, a gyro sensor 240B, an air pressure sensor 240C, a magnetic sensor 240D, an acceleration sensor 240E, a grip sensor 240F, and a proximity sensor ( 240G), color sensor (240H) (e.g. RGB (red, green, blue) sensor), bio sensor (240I), temperature/humidity sensor (240J), light sensor (240K), or UV (ultra violet) ) may include at least one of the sensors 240M. Additionally or alternatively, the sensor module 240 may include, for example, an e-nose sensor, an electromyography (EMG) sensor, an electroencephalogram (EEG) sensor, an electrocardiogram (ECG) sensor, It may include an IR (infrared) sensor, an iris sensor, and/or a fingerprint sensor. The sensor module 240 may further include a control circuit for controlling one or more sensors included therein. In some embodiments, the electronic device 201 further includes a processor configured to control the sensor module 240, either as part of the processor 210 or separately, so that while the processor 210 is in a sleep state, The sensor module 240 may be controlled.

The input device 250 may include, for example, a touch panel 252 , a (digital) pen sensor 254 , a key 256 , or an ultrasonic input device 258 . The touch panel 252 may use at least one of, for example, a capacitive type, a pressure-sensitive type, an infrared type, or an ultrasonic type. Also, the touch panel 252 may further include a control circuit. The touch panel 252 may further include a tactile layer to provide a tactile response to the user. The (digital) pen sensor 254 may be, for example, part of a touch panel or may include a separate recognition sheet. Keys 256 may include, for example, hardware buttons, optical keys, or keypads. The ultrasonic input device 258 may detect ultrasonic waves generated from an input tool through a microphone (eg, the microphone 288) and check data corresponding to the detected ultrasonic waves.

The display 260 (eg, the display 160) may include a panel 262, a hologram device 264, a projector 266, and/or a control circuit for controlling them. Panel 262 may be implemented to be flexible, transparent, or wearable, for example. The panel 262 may include the touch panel 252 and one or more modules. According to one embodiment, the panel 262 may include a pressure sensor (or force sensor) capable of measuring the strength of a user's touch. The pressure sensor may be implemented integrally with the touch panel 252 or may be implemented as one or more sensors separate from the touch panel 252 . The hologram device 264 may display a 3D image in the air using interference of light. The projector 266 may display an image by projecting light onto a screen. The screen may be located inside or outside the electronic device 201 , for example. Interface 270 may include, for example, HDMI 272, USB 274, optical interface 276, or D-sub (D-subminiature) 278. Interface 270 may be included in, for example, communication interface 170 shown in FIG. 1 . Additionally or alternatively, the interface 270 may include, for example, a mobile high-definition link (MHL) interface, an SD card/multi-media card (MMC) interface, or an infrared data association (IrDA) compliant interface. there is.

The audio module 280 may, for example, convert a sound and an electrical signal in both directions. At least some components of the audio module 280 may be included in the input/output interface 145 shown in FIG. 1 , for example. The audio module 280 may process sound information input or output through, for example, the speaker 282, the receiver 284, the earphone 286, or the microphone 288. The camera module 291 is, for example, a device capable of capturing still images and moving images, and according to one embodiment, one or more image sensors (eg, a front sensor or a rear sensor), a lens, and an image signal processor (ISP) , or a flash (eg, LED or xenon lamp, etc.). The power management module 295 may manage power of the electronic device 201 , for example. According to one embodiment, the power management module 295 may include a power management integrated circuit (PMIC), a charger IC, or a battery or fuel gauge. A PMIC may have a wired and/or wireless charging method. The wireless charging method includes, for example, a magnetic resonance method, a magnetic induction method, or an electromagnetic wave method, and may further include an additional circuit for wireless charging, for example, a coil loop, a resonance circuit, or a rectifier. there is. The battery gauge may measure, for example, the remaining capacity of the battery 296, voltage, current, or temperature during charging. Battery 296 may include, for example, a rechargeable battery and/or a solar cell.

The indicator 297 may indicate a specific state of the electronic device 201 or a part thereof (eg, the processor 210), for example, a booting state, a message state, or a charging state. The motor 298 may convert electrical signals into mechanical vibrations and generate vibrations or haptic effects. The electronic device 201 is, for example, a mobile TV support device capable of processing media data according to standards such as digital multimedia broadcasting (DMB), digital video broadcasting (DVB), or mediaFlo ^TM (e.g., : GPU) may be included. Each of the components described in this document may be composed of one or more components, and the name of the corresponding component may vary depending on the type of electronic device. In various embodiments, an electronic device (eg, the electronic device 201) is configured as a single entity by omitting some components, further including additional components, or combining some of the components. The functions of the previous corresponding components may be performed identically.

In various embodiments of the present disclosure, the electronic device 201 (or the electronic device 101) may include a housing including a front surface, a rear surface, and a side surface surrounding a space between the front surface and the rear surface. A touch screen display (eg, the display 260) is disposed within the housing and may be exposed through the front surface. A microphone 288 is disposed within the housing and may be exposed through a portion of the housing. At least one speaker 282 is disposed within the housing and may be exposed through another part of the housing. A hardware button (eg, key 256) may be located on another part of the housing or configured to display on the touchscreen display. A wireless communication circuit (eg, the communication module 220) may be located in the housing. The processor 210 (or processor 120) is located in the housing and may be electrically connected to the touch screen display, the microphone 288, the speaker 282, and the wireless communication circuit. The memory 230 (or the memory 130 ) may be located in the housing and electrically connected to the processor 210 .

In various embodiments of the present invention, the memory 230 is set to store a first application program including a first user interface for receiving text input, and the memory 230, when executed, the The processor 210 stores instructions that cause a first operation and a second operation to be performed, and the first operation is performed through the button while the first user interface is not displayed on the touch screen display. Receive a first type of user input, and after receiving the first type of user input, receive a first user utterance through the microphone 288, automatic speech recognition (ASR) and intelligence First data for the first user utterance is provided to an external server including an intelligence system, and after providing the first data, the intelligence system responds to the first user utterance from the external server. Receives at least one command for performing a task generated by, and the second operation receives the first user input through the button while the first user interface is displayed on the touch screen display, , After receiving the first type of user input, receiving a second user speech through the microphone 288, providing second data for the second user speech to the external server, and After providing data, the server receives data about text generated by the automatic speech recognition from the second user utterance, but not commands generated by the intelligent system, and the first user You can input the text into the interface.

In various embodiments of the present invention, the button may include a physical key located on the side of the housing.

In various embodiments of the present invention, the first type of user input is after pressing the button once, pressing the button twice, pressing the button three times, or pressing the button once. It can be either a held press, or a two-time press and hold press on the button.

In various embodiments of the present invention, the instructions may further cause the processor to display the first user interface along with a virtual keyboard. The button may not be part of the virtual keyboard.

In various embodiments of the invention, the instructions further cause the processor 210 to receive, from the external server, data for text generated by ASR from the first user utterance within the first action. can cause

In various embodiments of the present invention, the first application program may include at least one of a note application program, an e-mail application program, a web browser application program, or a calendar application program.

In various embodiments of the present invention, the first application program includes a message application, and the instructions cause the processor 210 to, if a selected period of time after inputting the text, exceed the wireless communication circuitry. It can be further caused to transmit automatically input text through.

In various embodiments of the present invention, the instructions further cause the processor 210 to perform a third operation, wherein the third operation is while displaying the first user interface on the touchscreen display. , Receives a second type of user input through the button, receives a third user speech through the microphone after receiving the second type of user input, and responds to the third user speech by the external server. After providing the third data, at least one command for performing a task generated by the intelligent system in response to the third user utterance may be received from the external server. there is.

In various embodiments of the present invention, the instructions further cause the processor 210 to perform a fourth operation, wherein the fourth operation occurs when the first user interface is not displayed on the touch screen display. On the way, the second type of user input is received through the button, and after receiving the second type of user input, a fourth user speech is received through the microphone 288, and the fourth user speech is received. Provides fourth data for the external server to the external server, and after providing the fourth data, in response to the fourth user utterance, at least one command for performing a task generated by the intelligent system is provided. After receiving from an external server, receiving a fifth user speech through the microphone, providing fifth data for the fifth user speech to the external server, and providing the fifth data, 5 In response to user speech, at least one command for performing a task generated by the intelligent system may be received from the external server.

In various embodiments of the present invention, the first type of user input and the second type of user input are different, and include pressing the button once, pressing the button twice, and pressing the button three times. , Pressing and maintaining the button after pressing the button once, or pressing and maintaining the button twice and maintaining the button may be selected.

In various embodiments of the present invention, the memory 230 is further configured to store a second application program including a second user interface for receiving a text input, and the instructions, when executed, are configured by the processor ( 210) further causes a third operation to be performed, wherein the third operation receives a user input of the first type through the button while displaying the second user interface, and the user of the first type After the input is received, a third user speech is received through the microphone, third data for the third user speech is provided to the external server, and after the third data is provided, the external server inputting the text to the second user interface while receiving data about text generated by ASR from the third user utterance from the server, but not receiving a command generated by the intelligent system; and is input, and when the selected time period is exceeded, the inputted text may be automatically transmitted through the wireless communication circuit.

In various embodiments of the present invention, the memory 230 is set to store a first application program including a first user interface for receiving text input, and the memory 230, when executed, the The processor 210 stores instructions that cause a first operation and a second operation to be performed, the first operation receives a first type of user input through the button, and the first type of user input After receiving , the first user utterance is received through the microphone 288, and an external server including an automatic speech recognition (ASR) and intelligence system is configured to receive the first user utterance. Provide first data for, and, after providing the first data, receive at least one command for performing a task generated by the intelligent system in response to the first user utterance from the external server. and the second operation receives a second type of user input through the button and, after receiving the second type of user input, receives a second user utterance through the microphone 288; After providing the second data for the second user utterance to the external server, and receiving data for text generated by ASR from the second user utterance from the server after providing the second data, , the command generated by the intelligent system is not received, and the text can be input to the first user interface.

In various embodiments of the present invention, the instructions may further cause the processor 210 to display the first user interface along with a virtual keyboard, and the buttons may not be part of the virtual keyboard.

In various embodiments of the invention, the instructions further cause the processor 210 to receive data for text generated by the ASR from the first user utterance within the first action from the external server. can do.

In various embodiments of the present invention, the first application program may include at least one of a note application program, an e-mail application program, a web browser application program, or a calendar application program.

In various embodiments of the present invention, the first application program includes a message application, and the instructions cause the processor 210 to, if a selected period of time after inputting the text, exceed the wireless communication circuitry. It can be further caused to transmit automatically input text through.

In various embodiments of the present invention, the instructions may further cause the processor 210 to perform the first operation independently of display on the display of the first user interface.

In various embodiments of the present disclosure, the instructions may cause the processor 210 to perform the second operation when at least one of the electronic device is locked or the touch screen display is turned off. can cause more

In various embodiments of the present disclosure, the instructions may further cause the processor 210 to perform the second operation while displaying the first user interface on the touch screen display.

In various embodiments of the present invention, the memory 230, when executed, allows the processor 210 to receive user utterances through the microphone 288, perform automatic speech recognition (ASR) or An external server that performs at least one of natural language understanding (NLU), and performs the ASR on the user utterance data together with the user utterance data to obtain the natural language understanding for text obtained. transmits information associated with whether or not to perform the natural language understanding, and if the information indicates not to perform the natural language understanding, receive the text for data on the user's utterance from the external server; If indicated, an instruction causing the external server to receive a command obtained as a result of performing the natural language understanding of the text may be stored.

3 is a block diagram of program modules according to various embodiments. According to one embodiment, the program module 310 (eg, the program 140) is an operating system that controls resources related to an electronic device (eg, the electronic device 101) and/or various applications running on the operating system (eg, the electronic device 101). Example: application program 147). The operating system may include, for example, Android ^TM , iOS ^TM , Windows ^TM , Symbian ^TM , Tizen ^TM , or Bada ^TM . Referring to FIG. 3, the program module 310 includes a kernel 320 (eg kernel 141), middleware 330 (eg middleware 143), (API 360 (eg API 145) ), and/or an application 370 (eg, the application program 147). At least a part of the program module 310 is preloaded on an electronic device, or an external electronic device (eg, an electronic device ( 102, 104), server 106, etc.).

The kernel 320 may include, for example, a system resource manager 321 and/or a device driver 323 . The system resource manager 321 may perform control, allocation, or recovery of system resources. According to one embodiment, the system resource manager 321 may include a process management unit, a memory management unit, or a file system management unit. The device driver 323 may include, for example, a display driver, a camera driver, a Bluetooth driver, a shared memory driver, a USB driver, a keypad driver, a WiFi driver, an audio driver, or an inter-process communication (IPC) driver. . The middleware 330, for example, provides functions commonly required by the application 370 or provides various functions through the API 360 so that the application 370 can use limited system resources inside the electronic device. It can be provided as an application 370 . According to one embodiment, the middleware 330 includes a runtime library 335, an application manager 341, a window manager 342, a multimedia manager 343, a resource manager 344, a power manager 345, a database manager ( 346), a package manager 347, a connectivity manager 348, a notification manager 349, a location manager 350, a graphic manager 351, or a security manager 352.

The runtime library 335 may include, for example, a library module used by a compiler to add new functions through a programming language while the application 370 is being executed. The runtime library 335 may perform input/output management, memory management, or arithmetic function processing. The application manager 341 may manage the life cycle of the application 370 , for example. The window manager 342 may manage GUI resources used in the screen. The multimedia manager 343 may determine a format required for reproducing media files, and encode or decode the media files using a codec suitable for the format. The resource manager 344 may manage a source code of the application 370 or a memory space. The power manager 345 may manage, for example, battery capacity or power, and provide power information necessary for the operation of the electronic device. According to one embodiment, the power manager 345 may interoperate with a basic input/output system (BIOS). The database manager 346 may create, search, or change a database to be used in the application 370 , for example. The package manager 347 may manage installation or update of applications distributed in the form of package files.

The connectivity manager 348 may manage wireless connections, for example. The notification manager 349 may provide a user with an event such as an arrival message, an appointment, or proximity notification. The location manager 350 may manage, for example, location information of an electronic device. The graphic manager 351 may manage, for example, graphic effects to be provided to the user or user interfaces related thereto. Security manager 352 may provide system security or user authentication, for example. According to one embodiment, the middleware 330 may include a telephony manager for managing voice or video call functions of an electronic device or a middleware module capable of forming a combination of functions of the aforementioned components. . According to one embodiment, the middleware 330 may provide modules specialized for each type of operating system. The middleware 330 may dynamically delete some existing components or add new components. The API 360 is, for example, a set of API programming functions, and may be provided in different configurations depending on the operating system. For example, in the case of Android or iOS, one API set can be provided for each platform, and in the case of Tizen, two or more API sets can be provided for each platform.

The application 370 includes, for example, a home 371, a dialer 372, an SMS/MMS 373, an instant message (IM) 374, a browser 375, a camera 376, and an alarm 377. , Contacts (378), Voice Dial (379), Email (380), Calendar (381), Media Player (382), Album (383), Watch (384), Health Care (e.g. exercise or blood sugar measurement) , or environmental information (eg, air pressure, humidity, or temperature information) providing applications. According to one embodiment, the application 370 may include an information exchange application capable of supporting information exchange between an electronic device and an external electronic device. The information exchange application may include, for example, a notification relay application for delivering specific information to an external electronic device or a device management application for managing an external electronic device. For example, a notification delivery application may transfer notification information generated by another application of an electronic device to an external electronic device or may receive notification information from an external electronic device and provide the notification information to a user. The device management application is, for example, a function of an external electronic device communicating with the electronic device (eg, turn-on/turn-off of the external electronic device itself (or some component parts) or display brightness (or resolution)). adjustment), or an application operating in an external electronic device may be installed, deleted, or updated. According to an embodiment, the application 370 may include an application designated according to the properties of an external electronic device (eg, a health management application of a mobile medical device). According to one embodiment, the application 370 may include an application received from an external electronic device. At least a portion of the program module 310 may be implemented (eg, executed) in software, firmware, hardware (eg, the processor 210), or a combination of at least two of them, and a module for performing one or more functions; It can include a program, routine, set of instructions, or process.

The term "module" used in this document includes a unit composed of hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. A “module” may be an integrally constructed component or a minimal unit or part thereof that performs one or more functions. A "module" may be implemented mechanically or electronically, for example, a known or future developed application-specific integrated circuit (ASIC) chip, field-programmable gate arrays (FPGAs), or A programmable logic device may be included. At least some of the devices (eg, modules or functions thereof) or methods (eg, operations) according to various embodiments are instructions stored in a computer-readable storage medium (eg, the memory 130) in the form of program modules. can be implemented as When the command is executed by a processor (eg, the processor 120), the processor may perform a function corresponding to the command. Computer-readable recording media include hard disks, floppy disks, magnetic media (e.g. magnetic tape), optical recording media (e.g. CD-ROM, DVD, magneto-optical media (e.g. floptical disks), built-in memory, etc.) A command may include code generated by a compiler or code executable by an interpreter A module or program module according to various embodiments may include at least one or more of the above-described components or , some may be omitted, or may further include other elements.According to various embodiments, operations performed by modules, program modules, or other elements may be executed sequentially, in parallel, iteratively, or heuristically, or at least Some actions may be performed in a different order, omitted, or other actions may be added.

And the embodiments disclosed in this document are presented for explanation and understanding of the disclosed technical content, and do not limit the scope of the present disclosure. Therefore, the scope of the present disclosure should be construed to include all changes or various other embodiments based on the technical spirit of the present disclosure.

Claims (5)

In electronic devices, contains a processor; the processor, Enter the mass spectrum of the polypeptide obtained by hydrolyzing the glycoprotein in the sample and the mass spectrum of the glycopeptide to be identified into a DNN (Deep Neural Network)-based workflow recommendation artificial intelligence model, Determining a first mass spectrum having the highest similarity to the mass spectrum of the polypeptide among a plurality of pre-stored first mass spectra using the workflow recommendation artificial intelligence model; Determining a second mass spectrum having the highest similarity to the mass spectrum of the glycopeptide among a plurality of previously stored second mass spectra associated with the first mass spectrum using the workflow recommendation artificial intelligence model, and An electronic device configured to recommend a workflow specified by the second mass spectrum and a parameter set to be used in each step of the workflow using the workflow recommendation artificial intelligence model. According to claim 1, [Equation 1]

the processor, Using Equation 1, it is set to determine the first mass spectrum having the highest similarity with the mass spectrum of the polypeptide or determining the second mass spectrum having the highest similarity with the mass spectrum of the glycopeptide, S _i is a (x,y) matrix, x represents the n-th relative peak intensity, y represents the mass of the n-th peak, S' _i is a (x',y') matrix, and x' is Represents the n-th relative peak intensity, and y' represents the mass of the n-th peak. According to claim 2, the processor, Monitoring the process of identifying the mass spectrum of the target glycopeptide from the mass spectrum of the target polypeptide, and The electronic device configured to determine whether the similarity of the mass spectrum of the target polypeptide is equal to or less than a first value with respect to each of the plurality of first mass spectra in the workflow recommendation artificial intelligence model. According to claim 3, the processor, For all of the plurality of first mass spectra, when the mass spectrum similarity of the target polypeptide is equal to or less than a first value: updating the workflow recommendation artificial intelligence model to add the mass spectrum of the target polypeptide to a new category of the first plurality of mass spectra; and to relate the mass spectrum of the target glycopeptide to the mass spectrum of the target polypeptide, and to associate a specific workflow used in the identification process and a set of parameters used in the specific workflow to the mass spectrum of the target glycopeptide An electronic device that updates the workflow recommendation artificial intelligence model. According to claim 4, the processor, For at least a portion of the plurality of first mass spectra, when the similarity of mass spectra of the target polypeptide exceeds the first value: For each of the plurality of third mass spectra associated with a mass spectrum having the highest similarity with the mass spectrum of the target polypeptide among the at least part of the first mass spectrum, the similarity of the mass spectrum of the target glycopeptide is equal to or less than the second value. An electronic device configured to determine cognition.

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