Abstract: Provided is a method for detecting a biomarker indicating states of a target biological system based on data acquired by measuring the target biological system. The method includes the steps of: preparing a reference dataset based on data acquired from one or more reference biological systems; generating a target dataset by adding, to the reference dataset, target biological data acquired from the target biological system; acquiring first correlation coefficients between a plurality of factor items in the reference dataset; acquiring second correlation coefficients between the plurality of factor items in the target dataset; acquiring difference correlation coefficients that are differences between the first correlation coefficients and the second correlation coefficients; acquiring indexes respectively for the plurality of factor items based on the difference correlation coefficients; and selecting the biomarker based on the indexes.

Abstract: The invention discloses a multi-step prediction method and system of future wind speed based on automatic reservoir neural network, realizes accurate and fast multi-step prediction of future information, maintains high robustness to noise and system time-varying, and avoids over-fitting problems. The technical scheme is: for short-term high-dimensional wind speed data, based on the delay embedding theory, the observed high-dimensional dynamics is used as the reservoir by using space-time information transformation, and the high-dimensional wind speed data is mapped to the future information of the target variable. The automatic reservoir neural network realizes the multi-step prediction of the target variable by solving a pair of conjugate space-time information interaction equations.

Abstract: The present invention discloses an anticipated learning method and system for short-term time series prediction, which solves the prediction problem of short-term high-dimensional time series and realizes accurate multi-step prediction of short-term high-dimensional data. The technical proposal is as follows: selecting a variable for prediction from time series data, performing anticipated learning for short-term time series prediction on basis of two trained neural network models, and finally outputting a portion of the selected prediction variables that needs to be predicted.

Abstract: The present invention provides a device, method, and program for detection of a biomarker candidate that may be used in a diagnosis of a pre-disease state indicating a transition from a healthy state to a disease state. Biological samples are collected from a subject to be measured at different times. Statistical data is obtained by aggregating measurement data obtained in measurement on collected biological samples. Thereafter, a process of obtaining high-throughput data (s1), a process of choosing differential biological molecules (s2), a process of clustering (s3), a process of choosing a DNB candidate (s4), and a process of identifying a DNB by significance analysis (s5) are carried out.

Abstract: Provided is a method for detecting a biomarker indicating states of a target biological system based on data acquired by measuring the target biological system. The method includes the steps of: preparing a reference dataset based on data acquired from one or more reference biological systems; generating a target dataset by adding, to the reference dataset, target biological data acquired from the target biological system; acquiring first correlation coefficients between a plurality of factor items in the reference dataset; acquiring second correlation coefficients between the plurality of factor items in the target dataset; acquiring difference correlation coefficients that are differences between the first correlation coefficients and the second correlation coefficients; acquiring indexes respectively for the plurality of factor items based on the difference correlation coefficients; and selecting the biomarker based on the indexes.

Abstract: The invention provides a detection device, method, and program capable of highly accurately detecting a pre-disease state that indicates a precursor to a state transition from a healthy state to a disease state. The following processes are carried out: a process of obtaining measured data on genes, proteins, etc. related to a biological object as high-throughput data (s1), a process of selecting differential biological molecules (s2), a process of calculating the SNE of a local network (s3), a process of selecting a biomarker candidate (s4), a process of calculating an average SNE across the entire network (s5), and a process of determining and detecting whether or not the system is in a pre-disease state (s6).

Abstract: The invention provides a detection device, method, and program capable of highly accurately detecting a pre-disease state that indicates a precursor to a state transition from a healthy state to a disease state. The following processes are carried out: a process of obtaining measured data on genes, proteins, etc. related to a biological object as high-throughput data (s1), a process of selecting differential biological molecules (s2), a process of calculating the SNE of a local network (s3), a process of selecting a biomarker candidate (s4), a process of calculating an average SNE across the entire network (s5), and a process of determining and detecting whether or not the system is in a pre-disease state (s6).

Abstract: The present invention provides a device, method, and program for detection of a biomarker candidate that may be used in a diagnosis of a pre-disease state indicating a transition from a healthy state to a disease state. Biological samples are collected from a subject to be measured at different times. Statistical data is obtained by aggregating measurement data obtained in measurement on collected biological samples. Thereafter, a process of obtaining high-throughput data (s1), a process of choosing differential biological molecules (s2), a process of clustering (s3), a process of choosing a DNB candidate (s4), and a process of identifying a DNB by significance analysis (s5) are carried out.

Abstract: There is provided an available transfer capability calculating system for operating an electric power system, calculating optimal power flow with respect to assumption failure occurring in the electric power system and calculating available transfer capability of the electric power system based on the calculated value of the optimal power flow. The system has an optimal power flow calculating processor for deriving data associated with the initial phase angle and maximum electric power value of a generator by calculating mechanical output and electrical output of a generator including a generator phase angle defined by a time function in a condition that the generator phase angle does not exceed a preset value.