Abstract: Technologies are disclosed for determining validity of data obtained from an A/B experiment, where the experiment evaluates the desirability of a potential change at a website. The experiment is run for a period of time and based upon an expected allocation of users into the A group (control group) and the B group (e.g., treatment group), along with an actual number of users directed into the two groups, it is determined if a minimal detectable error of allocation has been exceeded and, if it has, the data is deemed to be invalid. If not, the data is deemed to be valid.

Abstract: A computer-facilitated service selects, in response to a request to access user interface, an interaction element that can be presented via the user interface. The computer-facilitated service records information involving user interactions with the interaction element presented via the user interface. A model is applied to the recorded information to determine a classification of the user from a set of classifications that comprises human users and automated agents. The computer-facilitated service records an association between the user and the classification.

Abstract: Technologies are disclosed for determining network-accessible content changes based on computed models and providing a long term forecast of user interaction at a network accessible site based upon a short term experiment at the site. A forecast model for a period of time is generated based upon historical data of user interactions at the site. An experiment is run for a short term at the site based upon a potential change at the site. Based upon data obtained during the experiment, scores are generated for a control group (no change) and a treatment group (potential change) and compared. If there are statistically significant differences between the control group and the treatment group scores, the long term forecast may be used to forecast what the long term impact of the experiment would be based upon the short term experiment.

Abstract: Described herein is a system and method for processing analytics data in a windowed manner. In some embodiments, each user of a number of users is assigned to a group (e.g., a treatment group or a control group). The system may monitor each user to obtain metric data associated with each of the users over the course of an experiment. In some embodiments, the system may identify an indication of a trigger event with respect to each user. For example, the indication of the trigger event may comprise a time at which an event occurred for that user. The system may identify and compile data from the metric data for each of the users with respect to the trigger event. In some embodiments the metric data may be aggregated for each of the users with respect to a window or timespan positioned relative to the identified trigger event.

Abstract: Technologies are disclosed for determining a runtime length for an A/B experiment, where the experiment evaluates the desirability of a potential change at a website. The experiment is run for an initial period of time and based upon initial data from the initial period of time, an equation is iteratively solved until a minimal amount of time is determined that that indicates when a statistically significant change will be observed in the data. The experiment is then run for the minimal amount of time.

Abstract: Described are techniques for determining long-term effects of an experimental change to a user experience after the end of the experiment. A control state and a treatment state of a statistical hypothesis experiment may be assigned to first and second client devices, respectively, during an experiment time period. Subsequent to the end of the experiment, presentation of the control state may be discontinued. Result data corresponding to the treatment state may be determined during the experiment time period and for a length of time subsequent to the experiment time period. Result data corresponding to the control state may be determined during the experiment time period, and for a length of time prior to assignment of the control state to the first client device.

Abstract: One aspect of the present invention relates to methods of identifying cortisone response signature gene sets and methods of using the identified gene sets to identify compounds that modulate HSD1 activity. In some embodiments, methods are provided to use cortisone response gene sets to estimate the HSD1 activity. Another aspect of the present inventive relates to methods for identification of off-target signature gene sets that can be used to estimate HSD1 compound induced off-target activity and methods for classification of compounds that modulate HSD1 activity. Another aspect of the present invention relates to cell lines that over-expresses HSD1 and methods of use thereof. Additional embodiments of the of the invention are described in the specification provided herein. The contents of this ABSTRACT are not intended to in anyway limit the scope of the invention claimed herein.