| 摘要 |
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. The exemplary characteristics associated with cause and effects of the causal relationships can include an associated average difference that a cause can make to an effect in relation to each other cause of that effect. |
| 主权项 |
1. A computer implemented method for determining at least one causal relationship of at least two elements associated with time course data, comprising:
receiving data associated with a plurality of particular causal relationships; for each causal relationship, using at least one hardware computing arrangement, determining average characteristics associated with cause and effects of the particular causal relationships; and identifying, using a false discovery rate control procedure, the particular causal relationships that meet at least one predetermined requirement as a function of the average characteristics so as to generate at least one causal relationship.
|
