Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel to detect cancer and particular types of cancer.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel for detection of cancer tissue of origin (e.g., types of cancer).

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel to detect cancer and particular types of cancer.

Abstract: The present description provides a hematological disorder (HD) assay panel for targeted detection of methylation patterns or variants specific to various hematological disorders, such as clonal hematopoiesis of indeterminate potential (CHIP) and blood cancers, such as leukemia, lymphoid neoplasms (e.g. lymphoma), multiple myeloma, and myeloid neoplasm. Further provided herein includes methods of designing, making, and using the HD assay panel for detection of various hematological disorders.

Abstract: Techniques for controlling resource usage in a computing environment are provided. In one technique, a target resource usage for a particular point in time is determined for a content delivery campaign. Determining, for the content delivery campaign, a current resource usage for the particular point in time. Also, a bandwidth associated with the target resource usage at the particular point in time is determined. Based on a difference between the current resource usage and one or more boundaries of the bandwidth, a throttling factor is calculated. Based on the throttling factor, a probability of the content delivery campaign participating in a content item selection event is determined.

Abstract: In various embodiments, an analytics system uses models to determine features and classification of disease states. A disease state can indicate presence or absence of cancer, a cancer type, or a cancer tissue of origin. The models can include a binary classifier and a tissue of origin classifier. The analytics system can process sequence reads from test biological samples to generate data for training the classifiers. The analytics system can also use combinations of machine learning techniques to train the models, which can include a multilayer perceptron. In some embodiments, the analytics system uses methylation information to train the models to determine predictions regarding disease state.

Abstract: A system, method, and apparatus are provided for migrating contacts (e.g., records identifying contacts) from one domain to another. Each record includes various fields corresponding to a single contact or other entity known within the first domain. The migration process may involve configuring multiple queries from the field data, for querying the second domain or system to identify (or to attempt to identify) matching entities known within the second domain. One or more exact queries may be configured to identify specific entities within the second domain, based on entity-specific data, for example. Other queries may be more general and may match multiple entities known within the second domain. For records for which no exact query identifies an entity, one or more general queries may be executed to identify multiple entities, which may be subsequently ranked based on the likelihood that they are the entity that corresponds to the record.

Abstract: Techniques are provided for generating recommendations to improve the delivery of electronic content items over one or more networks. In one technique, multiple predictive functions are stored, each associated with a different objective and based on multiple features. Multiple feature values of a first content delivery campaign are identified. For each feature value of the multiple feature values, a second feature value that is different than said each feature value is identified and input into each predictive function of the multiple predictive functions to generate multiple outputs, each output corresponding to a different predictive function of the multiple predictive functions. The multiple outputs are combined to generate a predicted performance. Based on the predicted performance associated with each feature value of the multiple feature values, a particular feature is identified and presented on a screen of a computing device.

Abstract: A system and method for determining whether a computer system is experiencing a problem are provided. Multiple data sets are identified. Each data set includes multiple values indicating a set of attributes that relate to a computer system and that correspond to a different time period of multiple time periods, such as days or hours. A model is generated based on the multiple data sets. A particular data set is identified that includes a set of values that correspond to the set of attributes and a particular time period that is subsequent to each of the multiple time periods. The model is used to generate a predicted value based on the particular data set. An actual value that corresponds to the particular time period is identified. A difference between the actual value and the predicted value is calculated and indicates a likelihood that the computer system is experiencing a problem.

Abstract: Techniques for controlling resource usage in a computing environment are provided. In one technique, a target resource usage for a particular point in time is determined for a content delivery campaign. Determining, for the content delivery campaign, a current resource usage for the particular point in time. Also, a bandwidth associated with the target resource usage at the particular point in time is determined. Based on a difference between the current resource usage and one or more boundaries of the bandwidth, a throttling factor is calculated. Based on the throttling factor, a probability of the content delivery campaign participating in a content item selection event is determined.

Abstract: A system and method for determining whether a computer system is experiencing a problem are provided. Multiple data sets are identified. Each data set includes multiple values indicating a set of attributes that relate to a computer system and that correspond to a different time period of multiple time periods, such as days or hours. A model is generated based on the multiple data sets. A particular data set is identified that includes a set of values that correspond to the set of attributes and a particular time period that is subsequent to each of the multiple time periods. The model is used to generate a predicted value based on the particular data set. An actual value that corresponds to the particular time period is identified. A difference between the actual value and the predicted value is calculated and indicates a likelihood that the computer system is experiencing a problem.

Abstract: Disclosed in some examples, are methods, systems, and machine readable mediums which allow for providing estimated impressions for content given arbitrary frequency caps. Time series historical visit data about each targeted user group is condensed by calculating, for each user in a targeted user group, an arrival rate. The arrival rates for each user in the targeted user group are used to construct a distribution of arrival rates in the user group. Given an arbitrary frequency cap, the system samples a large number of arrival rates N from the targeted user group. For each of the N sampled arrival rates, a time series corresponding to the arrival rate is created from that arrival rate and a frequency cap is applied to the sampled time series' to arrive at an estimated impression count. Adding up the frequency capped impressions for each sampled arrival rate and normalizing it for the number of members in the targeted population yields a prediction of the number of impressions in a given time period.

Abstract: In order to expand the audience for an advertising campaign, a system identifies an expanded audience for the advertising campaign based on characteristics of individuals in the expanded audience and a target audience of the advertising campaign. Then, the system compares a historical cumulative advertising performance metric at a current time for the target audience with a current cumulative advertising performance metric at the current time for the target audience in the advertising campaign. Next, the system selectively changes a probability of showing advertisements in the advertising campaign to individuals in the expanded audience based on the comparison. For example, if a current cumulative number of daily advertising impressions at the current time is less than a historical cumulative number of daily advertising impressions at a current time, the system increases the probability.

Abstract: Techniques for forecasting for an advertisement campaign are described. A personalized communication system can receive a request for an advertisement campaign on a social network. The request can have a member attribute and a time frame. The personalized communication system can access member data and behavior data from the social network. Additionally, the personalized communication system can determine a target group based on the member data and the member attribute. Furthermore, the personalized communication system can calculate a number of unique visitors to the social network from the target group based on the member attribute, the time frame, and a frequency cap. Subsequently, the personalized communication system can forecast a number of messages for the first advertisement campaign based on the calculated number of unique visitors, the behavior data, and the time frame.