Abstract: Time-course data with an underlying causal structure may appear in a variety of domains, including, e.g., neural spike trains, stock price movements, and gene expression levels. Provided and described herein are methods, procedures, systems, and computer-accessible medium for inferring and/or determining causation in time course data based on temporal logic and algorithms for model checking. For example, according to one exemplary embodiment, the exemplary method can include receiving data associated with particular causal relationships, for each causal relationship, determining average characteristics associated with cause and effects of the causal relationships, and identifying the causal relationships that meet predetermined requirement(s) as a function of the average characteristics so as to generate a causal relationship.

Abstract: Exemplary embodiments of exemplary methods, procedures, computer-accessible medium and systems according to the present disclosure can be provided which can be used for determining token causality. For example, data which comprises token-level time course data and type-level causal relationships can be obtained. In addition, a determination can be made as to whether the type-level causal relationships are instantiated in the token-level time course data, and using a computing arrangement. Further, exemplary significance scores for the causal relationships can be determined based on the determination procedure. It is also possible to determine probabilities associated with the type-level causal relationships using the token-level time course data and a probabilistic temporal model and/or type-level time course data when at least one of the type-level causal relationships have indeterminate truth values.

Abstract: Exemplary embodiments of system, computer-accessible medium and method can be provided for organizing or analyzing at least two sets of data. The sets of data can be organized and/or analyzed by generating a data structure for the sets of data and comparing the data structure for the at least two sets of data. The data structure can be in the form of a phylogenetic-type tree, and at least one of the sets of the data can include time series data.

Abstract: Time-course data with an underlying causal structure may appear in a variety of domains, including, e.g., neural spike trains, stock price movements, and gene expression levels. Provided and described herein are methods, procedures, systems, and computer-accessible medium for inferring and/or determining causation in time course data based on temporal logic and algorithms for model checking. For example, according to one exemplary embodiment, the exemplary method can include receiving data associated with particular causal relationships, for each causal relationship, determining average characteristics associated with cause and effects of the causal relationships, and identifying the causal relationships that meet predetermined requirement(s) as a function of the average characteristics so as to generate a causal relationship.

Abstract: According to exemplary embodiments of the present invention, system, computer-accessible medium and method of organizing or analyzing at least two sets of data can be provided. For example, at least two sets of data may be organized or analyzed by generating a data structure for the at least two sets of the data and comparing the data structure for the at least two sets of the data. The data structure may be in the form of a phylogenetic-type tree.