Abstract: Described herein are technologies pertaining to computationally-efficiently performing genome-wide association studies. Feature selection methods are used to identify genetic markers for addressing potential confounding in the data. Then, single SNPs, or groups of genetic markers are analyzed to ascertain whether such groups are causal or tagging of causal as to a specified phenotype, after taking in to account the feature-selected SNPs. Group and univariate analysis is accomplished by way of analyzing a group of genetic markers conditioned upon other genetic markers that are found to be predictive of the specified phenotype.

Abstract: Described herein are technologies pertaining to predicting whether a living being, such as a human being, an animal, or a plant, has a phenotype or set of phenotypes in real-time or near real-time. A filter set of genetic markers are determined heuristically, by first univariately computing scores for respective genetic markers that are indicative of their predictive ability with respect to the phenotype or the set of phenotypes. Thereafter, during training, the filter set is initially selected and thereafter expanded based upon the scores, until predictive accuracy for the phenotype or set of phenotypes reaches a threshold or is optimized. The filter set, which includes a relatively small number of genetic markers, is subsequently employed for real-time or near-real time phenotype prediction.

Abstract: A computer-executable algorithm that estimates parameters of a predictive model in computation time of less than O(n2k2) when k<=n, is described herein, wherein n is a number of data items considered when estimating the parameters of the predictive model and k is a number of features of each data item considered when estimating the parameters of the predictive model. The parameters are estimated to maximize the probability of observing target values in the training data given the features considered in the training data.

Abstract: A system described herein includes a receiver component that receives an HLA data set, wherein the HLA data set comprises low resolution HLA data. An HLA refinement component comprises a statistical model that automatically refines the HLA data set to transform the low resolution HLA data to high resolution HLA data.

Abstract: A system for optimizing the value of communications between communicating parties is provided. The system includes a communication group manager that facilitates specifying policies, preferences and/or automated analysis of ideal communication channels, routing and/or scheduling in terms of communicating party groups that can be pre-populated clusters of communicating parties, assembled based on relationships (e.g., organizational), and/or assembled based on satisfying inclusion criteria (e.g., age, location, competence, communication history, meeting history). The communication group manager maps communicating parties into predefined and/or dynamically created groups that facilitate specifying and/or automatically computing ideal communication actions like selecting a channel, displaying lists of potential channels sorted by communicating party preferences, and (re)scheduling communications to different channels and/or times.

Abstract: Epitope prediction models are described herein. By way of example, a system for predicting epitope information relating to a epitope can include a classification model (e.g., logistic regression model). The trained classification model can illustratively operatively execute one ore logistic functions on received protein data, and incorporate one or more of hidden binary variables and shift variables that when processed represent the identification (e.g., prediction) of one or more desired epitopes. The classification model can be configured to predict the epitope information by processing data including various features of an epitope, MHC, MHC supertype, and Boolean combinations thereof.

Abstract: Statistical models for identifying associations are described herein. By way of example, a system for identifying associations between variables can include a model builder and an association identifier. The model builder can receive observations about the variables and generate a null model and a non-null model. The association identifier can assess the strength of the association between the variables by determining how much the non-null model better explains the observed data than the null model. Additionally or alternatively, the structure of the observed data can be inferred simultaneously with the statistical model.

Abstract: The present invention relates to a system (10, 200) and methodology (74) to enable a plurality of information associated with electronic messages, for example, to be automatically prioritized by a priorities system (12, 230) for transmittal to a user or system. The priorities system (12,230) can employ classifiers (20) that can be explicitly and/or implicitly trained to prioritize one or more received messages (14) according to a learned importance to the user. As an example, messages (14) can be classified as high, medium, low or other degrees of importance via a training set of examples (30) or types of messages having similar degrees of importance. A background monitor (34) can be provided to monitor a user's activities regarding message processing to further refine or tune the classifier (20) according to the user's personal decisions relating to message importance.

Abstract: A system comprising a machine learning classifier trained on a plurality of associations between a host and a pathogen to predict a pathogen characteristic is described herein. The pathogen characteristic can relate to a disease state of the host. Computer-executable instructions for performing a method of forecasting a portion of a target molecule anticipated to influence an organism's condition also are described herein. The method comprises employing population data to automatically analyze one or more areas of the target molecule to determine the portion of the target molecule anticipated to influence the organism's condition. The population data can pertain to at least one relationship between at least one diverse organism trait and the target molecule. One or more epitopes forecast by employing the method also are contemplated.

Abstract: The present invention relates to a system and methodology to enable a variety of information associated with one or more notification sources to be directed to one or more notification sinks via a notification platform architecture. The architecture includes a context analyzer for determining a user's state such as location and attentional focus, wherein the user's state is employed by a notification manager to make decisions regarding what, when and how information generated by the notification sources should be forwarded to the notification sinks, for example. These decisions can include a cost benefit analysis wherein considerations are given as to whether the benefits of notifying the user are outweighed by the costs of disrupting the user. Decision-theoretic policies and/or somewhat less formal heuristic policies can be employed to enable the decision-making process within the notification manager.

Abstract: The claimed subject matter provides systems and/or methods that determines a number of non-spurious arcs associated with a learned graphical model. The system can include devices and mechanisms that utilize learning algorithms and datasets to generate learned graphical models and graphical models associated with null permutations of the datasets, ascertaining the average number of arcs associated with the graphical models associated with null permutations of the datasets, enumerating the total number of arcs affiliated with the learned graphical model, and presenting a ratio of the average number of arcs to the total number of arcs, the ratio indicative of the number of non-spurious arcs associated the learned graphical model.

Abstract: The subject invention provides a unique system and method that facilitates mitigation of storage abuse in connection with free storage provided by messaging service providers such as email, instant messaging, chat, blogging, and/or web hosting service providers. The system and method involve measuring the outbound volume of stored data. When the volume satisfies a threshold, a cost can be imposed on the account to mitigate the suspicious or abusive activity. Other factors can be considered as well that can modify the cost imposed on the cost such as by increasing the cost. Machine learning can be employed as well to predict a level or degree of suspicion. The various factors or the text of the messages can be used as input for the machine learning system.

Abstract: The present invention relates to a system and methodology to facilitate multiattribute adjustments and control associated with messages and other communications and informational items that are directed to a user via automated systems. An interface, specification language, and controls are provided for defining a plurality of variously configured groups that may attempt to communicate respective items. Controls include the specification of priorities and preferences as well as the modification of priorities and preferences that have been learned from training sets via machine learning methods. The system provides both a means for assessing parameters used in the control of messaging and communications and for the inspection and modification of parameters that have been learned autonomously.

Abstract: A system described herein includes a receiver component that receives an HLA data set, wherein the HLA data set comprises low resolution HLA data. An HLA refinement component comprises a statistical model that automatically refines the HLA data set to transform the low resolution HLA data to high resolution HLA data.

Abstract: The present invention is related to a system and method of considering time segments or intervals in a collaborative filtering model. The present invention extends collaborative filtering approaches by integrating considerations of temporality into the training and/or vote input associated with the usage of collaborative filtering models. The present invention also applies filtering to the output with temporal models, so as to view a most appropriate subset of recommended content, centering on content that may be available at a target time. The present invention applies time to a collaborative filtering model by allowing weight to be associated with selections within a current time segment, selections historically watched within the current time segment by the user and selections historically watched within the current time segment by a large group of users.

Abstract: A system for optimizing the value of communications between communicating parties is provided. The system includes a communication group manager that facilitates specifying policies, preferences and/or automated analysis of ideal communication channels, routing and/or scheduling in terms of communicating party groups that can be pre-populated clusters of communicating parties, assembled based on relationships (e.g., organizational), and/or assembled based on satisfying inclusion criteria (e.g., age, location, competence, communication history, meeting history). The communication group manager maps communicating parties into predefined and/or dynamically created groups that facilitate specifying and/or automatically computing ideal communication actions like selecting a channel, displaying lists of potential channels sorted by communicating party preferences, and (re)scheduling communications to different channels and/or times.

Abstract: The present invention relates to a system and methodology to facilitate collaboration and communications between entities such as between automated applications, parties to a communication and/or combinations thereof. The systems and methods of the present invention include a service that supports collaboration and communication by learning predictive models that provide forecasts of one or more aspects of a users' presence and availability. Presence forecasts include a user's current or future locations at different levels of location precision and usage of different devices or applications. Availability assessments include inferences about the cost of interrupting a user in different ways and a user's current or future access to one or more communication channels. The predictive models are constructed from data collected by considering user activity and proximity from multiple devices, in addition to analysis of the content of users' calendars, the time of day, and day of week, for example.

Abstract: The subject invention provides a unique system and method that facilitates propagating selected advertisements among users of interactive services. Interactive service users can be targeted for specific types of advertisements for particular products or services. When a user selects at least one advertisement for more detailed viewing, the advertisement can be distributed to or shared with one or more other users. These other users may be part of the original user's social network. Thus user-selected advertisements can be shared among users who are familiar with each other's current or future interests. In some cases, user-selected advertisements can replace system-selected advertisements. As a result, advertisers can benefit from increased exposure of and interest in their advertisements.

Abstract: The present invention provides a unique system and method that can employ machine learning techniques to automatically fill one or more fields across a diverse array of web forms. In particular, one or more instrumented tools can collect input or entries of form fields. Machine learning can be used to learn what data corresponds to which fields or types of fields. The input can be sent to a central repository where other databases can be aggregated as well. This input can be provided to a machine learning system to learn how to predict the desired outputs. Alternatively or in addition, learning can be performed in part by observing entries and then adapting the autofill component accordingly. Furthermore, a number of features of database fields as well as constraints can be employed to facilitate assignments of database entries to form values—particularly when the web form has never been seen before by the autofill system.

Abstract: A system and method of caching data employing probabilistic predictive techniques that provides local storage of a subset of available viewing selections by assigning a value to a selection and retaining selections in the cache depending on the value and size of the selection. The value assigned to an item can represent the time-dependent likelihood that a user will review an item at some time in the future. An initial value of an item can be based on the user's viewing habits, the user's viewing habit over particular time segment and/or viewing habits of a group of user's during a particular time segment. A value assigned to a selection dynamically changes according to a set of cache retention policies, where the value can be time-dependent functions that decay based on the class of the item, as determined by inference about the class or via a label associated with the item.