Abstract: An analysis method may determine the presence of affection of at least any one of breast cancer, pancreatic cancer, lung cancer, gastric cancer, and colorectal cancer. The analysis method may include quantifying at least any one of hsa-miR-205-5p, hsa-miR-30e-5p, hsa-miR-106b-5p, hsa-miR-3613-5p, hsa-miR-483-5p, hsa-miR-574-3p, hsa-miR-125b-5p, hsa-miR-223-5p, hsa-miR-3613-3p, hsa-miR-941, hsa-miR-324-3p, hsa-miR-193a-5p, hsa-miR-4433a-3p, hsa-miR-29c-3p, hsa-miR-190a-5p, hsa-miR-885-5p, hsa-miR-194-5p, hsa-miR-29a-3p, hsa-miR-142-5p, hsa-miR-142-3p, hsa-miR-122-5p, hsa-miR-34a-5p, and hsa-miR-375-3p in a sample derived from an object.

Abstract: According to one embodiment, an information processing device includes a memory and one or more processors. The memory stores time-series data including one or more variables. The one or more processors are coupled to the memory and configured to: calculate a time derivative value of each of the variables; calculate a difference indicating fluctuation of a long-term component of the corresponding variable based on a designated time sample interval; estimate a coefficient of a linear regression equation by machine learning using the time derivative value and the difference as learning data; and output the linear regression equation.

Abstract: An information processing device according to an embodiment includes a hardware processor coupled to a memory. The hardware processor estimates morbidity representing a probability of a subject being suffering from a specific disease. The morbidity is estimated on the basis of: a first probability model representing a relation between a first physical quantity associated with the specific disease and a second physical quantity to be measured, a second probability model representing a relation between the first physical quantity and information about whether the subject is suffering from the specific disease, a prior probability of morbidity representing a probability of the subject being suffering from the specific disease in a situation where no information has been obtained with respect to the first physical quantity or the second physical quantity related to the subject, and the second physical quantity obtained by measuring the subject.

Abstract: According to an embodiment, an information processing device includes one or more processors. The processors calculate a first degree of influence of a plurality of variables on output data, and a frequency at which the plurality of variables are selected as a variable influencing the output data, based on K first models. The K first models are models estimated using a plurality of pieces of input data including the plurality of variables. The plurality of input data are obtained in K periods. K is an integer of 2 or more. The first model receives input of the input data including the plurality of variables and outputs the output data. The processors output the first degree of influence and the frequency in association with each other.

Abstract: An information processing apparatus of an embodiment includes one or more hardware processors. The one or more hardware processors receive input of parameter of a model to be estimated by machine learning and input of first input data. The one or more hardware processors train, by using the first input data as training data, the model using a cost function for which a cost is smaller as a change in the parameter is smaller.

Abstract: An information processing apparatus according to one embodiment includes one or more hardware processors connected to a memory. The hardware processors functions to store, in the memory, history information including identification information of a model and a history of updating the model. The model receives input data including variables and outputs output data. The variables are each a variable for which a rate of influence on the output data is calculated. The model has been updated by using first input data. The hardware processors functions to select a target model to be updated by using second input data. The target model is selected from among models identified by their respective identification information. The hardware processors functions to update the target model by performing transfer learning in which updated parameters are estimated by using the second input data.

Abstract: According to an embodiment, an information processing apparatus is configured to set a candidate for a time lag until analysis target data including at least one of a measurement item and a setting item for use in control of a process controller affects an objective variable, and a time-lag number allowed in a regression model; select, as a candidate for an explanatory variable, at least one of the measurement item measured at a time corresponding to the candidate for the time lag and the setting item set at the time; and determine a regularization parameter of the regression model such that a number of the time lag is equal to or less than the time-lag number, based on a regularization path indicating transition of a regression coefficient for the candidate for the explanatory variable, the regression coefficient varying in accordance with a value of the regularization parameter.

Abstract: According to an embodiment, an information processing device includes processors. The processors receive input of a plurality of pieces of input data obtained during K time periods. K is an integer equal to or greater than two. The processors estimate K first models. Each of the K first models receives input of input data and outputs output data. Each of the K first models is estimated for each period of the K time periods, using a plurality of pieces of input data obtained during the each period. The processors estimate a second model that indicates a relationship between first time parameters related to times of the K time periods, and the K first models. The processors estimate a first model corresponding to a specified second time parameter, based on the estimated second model.

Abstract: A prediction model generation device has a first storage unit that stores a plurality of explanatory variables, a second storage unit that stores a plurality of objective variables, an input unit that inputs instruction information on classification, a class generation unit that generates a plurality of classes based on the instruction information, and a prediction model calculation unit that calculates a plurality of prediction models corresponding to the plurality of classes. The prediction model calculation unit has a learning data set extraction unit that extracts a learning data set corresponding to each of the plurality of classes from among the plurality of explanatory variables and the plurality of objective variables.

Abstract: According to an embodiment, an information processing apparatus is configured to set a candidate for a time lag until analysis target data including at least one of a measurement item and a setting item for use in control of a process controller affects an objective variable, and a time-lag number allowed in a regression model; select, as a candidate for an explanatory variable, at least one of the measurement item measured at a time corresponding to the candidate for the time lag and the setting item set at the time; and determine a regularization parameter of the regression model such that a number of the time lag is equal to or less than the time-lag number, based on a regularization path indicating transition of a regression coefficient for the candidate for the explanatory variable, the regression coefficient varying in accordance with a value of the regularization parameter.

Abstract: An information processing apparatus includes a data acquisition unit that acquires data including a missing value, a missing rate calculation unit that calculates a missing rate indicating a ratio of missing values included in the data, and a covariance matrix estimation unit that estimates a covariance matrix based on the missing rate. According to the information processing apparatus, since the covariance matrix is estimated based on the missing rate, the estimation accuracy of the covariance matrix can be improved.

Abstract: A regression analysis apparatus includes a regression model constructor and a similar feature extractor. A regression model constructor is configured to construct a regression model that represents an objective variable with a plurality of explanatory variables that correspond to any one of a plurality of features and with a regression coefficient of the plurality of explanatory variables by performing regression analysis using analysis target data including the plurality of features with one of the plurality of features as the objective variable. A similar feature extractor is configured to calculate a similarity degree between a feature other than a feature that corresponds to the objective variable in the analysis target data and the plurality of explanatory variables, and each of the plurality of explanatory variables, and configured to extract a similar feature having the similarity degree higher than a predetermined value.

Abstract: An information processing apparatus of an embodiment includes one or more hardware processors. The one or more hardware processors receive input of parameter of a model to be estimated by machine learning and input of first input data. The one or more hardware processors train, by using the first input data as training data, the model using a cost function for which a cost is smaller as a change in the parameter is smaller.

Abstract: An information processing apparatus according to an embodiment of the present invention includes a group setting device configured to group a plurality of variables included in operation data into at least one type of groups, based on structure data representing a structural relation among the plurality of variables; a regularization term generator configured to generate at least one regularization term corresponding to the at least one type based on a coefficient for the variable included in the at least one type of groups, and a coefficient estimator configured to estimate, based on the operation data and an objective function including the at least one regularization term, values of a plurality of the coefficients for the plurality of variables.

Abstract: An information processing apparatus includes a data acquisition unit that acquires data including a missing value, a missing rate calculation unit that calculates a missing rate indicating a ratio of missing values included in the data, and a covariance matrix estimation unit that estimates a covariance matrix based on the missing rate. According to the information processing apparatus, since the covariance matrix is estimated based on the missing rate, the estimation accuracy of the covariance matrix can be improved.

Abstract: An information processing device includes: a first acquisition unit that acquires authorization omission information being information indicating how many save units among a plurality of save units in which object pieces of an object divided into a division number are stored respectively do not need authorization processing, based on the division number indicating how many object pieces the object is to be divided into and a restoration number being a number of object pieces required to restore the object; and a decision unit that decides a save unit which does not need the authorization processing from among the plurality of save units, based on the authorization omission information acquired by the first acquisition unit.

Abstract: According to one embodiment, a demand forecasting device includes a location model generator and an overall model generator. The location models generate each providing a forecasted value of demand for a geographical region including a plurality of locations, based on weather data for either of the plurality of locations and previous value of demand for the geographical region. The overall model generator generates an overall model based on the location models and coefficients that are determined based on size of impact to the forecasted value of demand, by the weather data of the locations.

Abstract: A prediction model generation device has a first storage unit that stores a plurality of explanatory variables, a second storage unit that stores a plurality of objective variables, an input unit that inputs instruction information on classification, a class generation unit that generates a plurality of classes based on the instruction information, and a prediction model calculation unit that calculates a plurality of prediction models corresponding to the plurality of classes. The prediction model calculation unit has a learning data set extraction unit that extracts a learning data set corresponding to each of the plurality of classes from among the plurality of explanatory variables and the plurality of objective variables.

Abstract: An analysis apparatus includes a regression model constructor and a similar feature extractor. A regression model constructor is configured to construct a regression model that represents a target variable with a plurality of explanatory variables that correspond to any one of a plurality of features and with a regression coefficient of the plurality of explanatory variables by performing regression analysis using analysis target data including the plurality of features with one of the plurality of features as the target variable. A similar feature extractor is configured to calculate a similarity degree between a feature other than the feature that corresponds to the target variable in the analysis target data and the plurality of explanatory variables, and each of the plurality of explanatory variables, and configured to extract a similar feature having the similarity degree higher than a predetermined value.

Abstract: An information processing device includes: a first acquisition unit that acquires authorization omission information being information indicating how many save units among a plurality of save units in which object pieces of an object divided into a division number are stored respectively do not need authorization processing, based on the division number indicating how many object pieces the object is to be divided into and a restoration number being a number of object pieces required to restore the object; and a decision unit that decides a save unit which does not need the authorization processing from among the plurality of save units, based on the authorization omission information acquired by the first acquisition unit.