Abstract: A device to support work of searching document data for interpreting an information analysis result of analysis data obtained by analyzing a sample containing an analyte, includes: an acquisition unit to acquire first information for identifying the analyte from the analysis data; a reception unit to receive input of second information for searching data of a document for interpreting the information analysis result of the analysis data; an extraction unit to extract, based on the first and second information, terms relevant to the information analysis result, from among terms in data of documents in a database; a calculation unit to calculate, for each relevant term, relevance scores indicating a relevance degree between the relevant term and the first information, and a relevance degree between the relevant term and the second information; and a processing unit to obtain an index value of statistical likelihood from the relevance scores.

Abstract: A training method includes: acquiring a pseudo noise waveform indicating an assumed noise; acquiring a pseudo peak waveform not including the pseudo noise; generating a pseudo signal waveform by adding the pseudo noise waveform and the pseudo peak waveform; and updating an estimation model based on the pseudo signal waveform, in which the acquiring the pseudo noise waveform includes: causing an analysis device to execute noise measurement generating the pseudo noise waveform a plurality of times; calculating a similarity degree of a plurality of pseudo noise waveforms generated by the noise measurement performed the plurality of times; and executing prescribed processing according to the similarity degree.

Abstract: An analysis device produces learning data for training processing of an estimation model More specifically, the analysis device obtains a plurality of reference waveforms for a given type of device. In addition, the analysis device specifies information about a peak for each of the plurality of reference waveforms according to a criterion corresponding to the given type of device. The analysis device assigns the specified information about the peak to each of the plurality of reference waveforms.

Abstract: A data generation device according to the present invention is a data generation device configured to simulatively generate data used when creating, by machine learning, a discriminator configured to detect a peak observed in a signal waveform, the data generation device including: a parameter frequency information acquisition unit configured to acquire information on frequency of a predetermined shape parameter which characterizes a shape of a signal waveform from a plurality of signal waveforms collected only in a target field of machine learning for creating the discriminator; and a simulated waveform generation unit configured to generate a simulated signal waveform which is able to include overlapping of a plurality of peaks and noise using the information on frequency of the shape parameter, in which the simulated signal waveform is provided as data for training or evaluating machine learning.

Abstract: An analysis device produces learning data for training an estimation model. More specifically, the analysis device obtains a plurality of reference waveforms. In addition, the analysis device specifies information about a peak for each of the plurality of reference waveforms according to a certain criterion. The analysis device assigns the specified information about the peak to each of the plurality of reference waveforms.

Abstract: A training support method includes: acquiring a first chromatogram; displaying a first chromatogram image; acquiring a second chromatogram identical or similar to the first chromatogram and second peak information specifying one or more peaks of the second chromatogram from a chromatogram DB; displaying a second chromatogram image and a second peak information image; receiving input, by a user, of first peak information specifying one or more peaks of the first chromatogram; and training an estimation model based on the first chromatogram and the first peak information.

Abstract: A certainty factor of peak picking can be calculated when the peak picking is performed using semantic segmentation technology. An analysis device divides a target waveform into a plurality of partial waveforms, determines a peak waveform that becomes a peak portion among the plurality of divided partial waveforms using a trained model, and calculates the certainty factor of a determination result of the peak waveform using data output from the trained model when the peak portion of the target waveform is determined using the trained model.

Abstract: A device to support work of searching document data for interpreting an information analysis result of analysis data obtained by analyzing a sample containing an analyte, includes: an acquisition unit to acquire first information for identifying the analyte from the analysis data; a reception unit to receive input of second information for searching data of a document for interpreting the information analysis result of the analysis data; an extraction unit to extract, based on the first and second information, terms relevant to the information analysis result, from among terms in data of documents in a database; a calculation unit to calculate, for each relevant term, relevance scores indicating a relevance degree between the relevant term and the first information, and a relevance degree between the relevant term and the second information; and a processing unit to obtain an index value of statistical likelihood from the relevance scores.

Abstract: A waveform analytical device 4 which analyzes a target waveform which is a chromatogram or an optical spectrum includes a waveform division unit 54 configured to divide the target waveform into a plurality of partial waveforms, a determination unit 55 configured to determine whether each of the plurality of partial waveforms of the target waveform is a peak portion using a learned model created by machine learning using a plurality of sets of a plurality of partial waveforms created by dividing a reference waveform having a peak portion whose position is known, and a classification unit 56 configured to classify the target waveform into a peak region where the peak portion continues and a non-peak region other than the peak region based on a determination result from the determination unit.

Abstract: A method for detecting a peak in data of a chromatogram or a spectrum, includes: detecting multiple tentative peaks in the data on the basis of a predetermined criterion; determining an actual measurement value of a predetermined feature value indicating a size of a tentative peak from each of the detected multiple tentative peaks, the feature value; determining a smoothed curve on the basis of respective horizontal axis values and actual measurement values of the multiple tentative peaks; determining a reference value of the feature value with respect to each of the multiple tentative peaks from the smoothed curve; and detecting, of the multiple tentative peaks, a tentative peak whose actual measurement value is within a predetermined range from the corresponding reference value as a true peak. Only tentative peaks whose actual measurement value is within a predetermined range from the corresponding reference value as a true peak.

Abstract: When chromatogram data for a target sample have been acquired, a peak position estimator determines an estimated result of the position of the starting and/or ending point of a peak as well as the confidence value representing the reliability of the estimation, using a trained model stored in the trained model storage section. Normally, a plurality of estimated results of the starting point and/or ending point of the peak are acquired for one peak. A peak information correction processor identifies a candidate having the highest confidence as a prime candidate, and superposes a plurality of candidates including the prime candidate, with their respective confidence values, on a displayed chromatogram. An operator referring to the confidence values selects a peak which needs close checking or correction, and corrects the starting point and/or ending point of the selected peak, for example, by selecting and indicating a candidate other than the prime candidate.

Abstract: The present invention is an analytical device (50) including: an information acquisition unit (51) configured to acquire first identification information identifying, from a result of measuring an analyte contained in a biological sample using an analyzer (10), the analyte; an extraction unit (56) configured to extract a related term related to the analyte, from a database (41) in which document data is accumulated, on a basis of the first identification information acquired by the information acquisition unit; and a presentation unit (57, 59) configured to present the related term extracted by the extraction unit to a user.

Abstract: An analyzer configured to acquire a chromatogram or spectrum by performing a predetermined analysis of a sample and perform a qualitative or quantitative analysis of components contained in the sample. The analyzer includes: a peak detection unit configured, based on information regarding a plurality of target components that need to be checked whether contained in the sample or that need to be quantified, to detect a peak or peaks in the chromatogram or spectrum acquired by the predetermined analysis of the sample corresponding to one of the target components, configured to acquire peak information regarding each of the peak or peaks, and configured to obtain confidence information for each of the peak or peaks, the confidence information being an indicative value of certainty of detecting a peak; and a display processing unit configured to display on a display unit a list of at least a part of the target components.

Abstract: A peak detecting method according to the present invention includes a first peak detecting step (S1) of detecting, with a first detection sensitivity, one or more peaks in object data whose peak is to be detected; and a second peak detecting step (S5) of detecting one or more peaks in the object data with a second detection sensitivity that is different from the first detection sensitivity. With this method, since two kinds of detection are performed at the different detection sensitivities, a shoulder peak (or a non-shoulder peak) candidate can be chosen from peaks that are detected with one of the detection sensitivities but are not detected with the other detection sensitivity, and thus, a shoulder peak can be appropriately detected with ease.

Abstract: A first reference line that is a regression line obtained from data within a predetermined range including a starting point of a peak detected from data of a graph showing changes in intensity with respect to a parameter, a second reference line that is a regression line obtained from data within a predetermined range including a ending point of the peak, and a third reference line connecting the starting and ending points, and one or more intermediate control points in a triangle defined by the first, second, and third reference lines are determined; and a Bezier curve between the starting point and the ending point is created to be determined to be a baseline of the peak, the Bezier curve being determined by control points of the starting point, the one or more intermediate control points, and the ending point in order on a parameter axis.

Abstract: An object is to accurately detect peaks of various compositions, even in a case of unseparated peaks in which peaks of a plurality of compositions are superimposed. A computer acquires waveform data D1 having a peak P1 in a composition A measured by a data analysis device (S10). Next, the computer acquires waveform data D2 having a peak P2 in a composition B measured by the data analysis device (S20). Next, waveform data D12 including unseparated peaks by superimposing the waveform data D1 including the acquired peak P1 and the waveform data D2 including the acquired peak P2 (S30) is generated. Next, the generated waveform data D12 of the unseparated peaks is input as learning data, and the waveform data D1 and D2 corresponding to the waveform data D12 are input as training data in Step S40. Next, machine learning is performed using the waveform data D12, D1, and D2, and a learned model for estimating an accurate separation method of unseparated peaks is constructed based on the trained result (S50).

Abstract: A model constructed by a training process using the technique of deep learning using the training data including images created from a large number of chromatograms and correct peak information is previously stored in a trained model storage section. When chromatogram data for a target sample acquired with an LC measurement unit are inputted, an image creator converts the chromatogram into an image and creates an input image in which one of the two areas divided by the chromatogram curve as the boundary in the image is filled. A peak position estimator inputs the pixel values of the input image into a trained model using a neural network, and obtains the position information of the starting point and/or ending point of the peak and a peak detection confidence as the output. A peak determiner determines the starting point and/or ending point of each peak based on the peak detection confidence.

Abstract: The peak detection method of the present invention is a method of detecting peaks from data of a graph representing change in measured value relative to a measurement variable, comprising: a wavelet transform step (S2) in which wavelet transform is performed on the aforementioned data using a mother wavelet having a single maximum value to find an evaluation function having said mother wavelet's scale and translation as parameters; and a peak candidate information acquisition step (S3 through S5) in which locations of peak candidates in the aforementioned data are found based on the translation at which said evaluation function has its maximum value, and the width of said peak candidates is determined based on the scale corresponding to said peak candidates. Performing wavelet transform makes it possible to detect peak candidates regardless of the strength or weakness of peaks, etc.

Abstract: When chromatogram data for a target sample have been acquired, a peak position estimator determines an estimated result of the position of the starting and/or ending point of a peak as well as the confidence value representing the reliability of the estimation, using a trained model stored in the trained model storage section. Normally, a plurality of estimated results of the starting point and/or ending point of the peak are acquired for one peak. A peak information correction processor identifies a candidate having the highest confidence as a prime candidate, and superposes a plurality of candidates including the prime candidate, with their respective confidence values, on a displayed chromatogram. An operator referring to the confidence values selects a peak which needs close checking or correction, and corrects the starting point and/or ending point of the selected peak, for example, by selecting and indicating a candidate other than the prime candidate.

Abstract: A model constructed by a training process using the technique of deep learning using the training data including images created from a large number of chromatograms and correct peak information is previously stored in a trained model storage section. When chromatogram data for a target sample acquired with an LC measurement unit are inputted, an image creator converts the chromatogram into an image and creates an input image in which one of the two areas divided by the chromatogram curve as the boundary in the image is filled. A peak position estimator inputs the pixel values of the input image into a trained model using a neural network, and obtains the position information of the starting point and/or ending point of the peak and a peak detection confidence as the output. A peak determiner determines the starting point and/or ending point of each peak based on the peak detection confidence.