Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: There is a need for solutions that classification solutions in hierarchical prediction domains. In one embodiment, this need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and/or unstructured fusion machine learning. In one particular example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: There is a need for solutions that classification solutions in hierarchical prediction domains. In one embodiment, this need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and/or unstructured fusion machine learning. In one particular example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: A method may include receiving an indication that an electronic alert is to be transmitted to a user; obtaining contextual information associated with the user; determining a first time for presenting the electronic alert to the user based in part on the contextual information; transmitting the electronic alert to a computing device associated with the user at the first time; storing the electronic alert in a message feed data structure; receiving a request to view the electronic alert at a second time, the second time occurring after the first time; in response to the request, retrieving the electronic alert from the message feed data structure; and transmitting the electronic alert at the second time.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: There is a need for solutions that classification solutions in hierarchical prediction domains. This need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and unstructured fusion machine learning. In one example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions are processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: A method may include receiving an indication that an electronic alert is to be transmitted to a user; obtaining contextual information associated with the user; determining a first time for presenting the electronic alert to the user based in part on the contextual information; transmitting the electronic alert to a computing device associated with the user at the first time; storing the electronic alert in a message feed data structure; receiving a request to view the electronic alert at a second time, the second time occurring after the first time; in response to the request, retrieving the electronic alert from the message feed data structure; and transmitting the electronic alert at the second time.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing/executing predictive data analysis using custom-parameterized dimensionality reduction. In one example, a method includes identifying a group of predictive input features and one or more predictive markers; determining a per-marker feature for each predictive marker; determining one or more refined features for the group of predictive input features based at least in part on each per-marker feature for a predictive marker; performing the predictive inference based at least in part on the one or more refined features to generate one or more predictions; and performing one or more prediction-based actions based at least in pat on the one or more predictions.

Abstract: A system, method, and computer media are provided for operating an interactive virtual retirement tool, comprising inputting user information into a memory of a computer system related to a plurality of quantifiable life goal elements, receiving a selection of one of the life goal elements, generating display information for the selected life goal element along with: a) image data associated with a value of the selected life goal element, and b) an adjustment control associated with the selected life goal element that allows modifying a value associated with the selected life goal element, receiving an adjustment from the adjustment control to modify the value to a new value, obtaining new image data associated with the new value using a processor of the computer system, and generating display information for the new image data.

Abstract: There is a need for solutions that classification solutions in hierarchical prediction domains. This need can be addressed by, for example, performing one or more online machine learning, co-occurrence analysis machine learning, structured fusion machine learning, and unstructured fusion machine learning. In one example, structured predictions inputs are processed in accordance with an online machine learning analysis to generate structurally hierarchical predictions and in accordance with a co-occurrence analysis machine learning analysis to generate structurally non-hierarchical predictions. Then, the structurally hierarchical predictions and the structurally non-hierarchical predictions in accordance with processed by a structured fusion model to generate structure-based predictions. Afterward, the structure-based predictions and non-structure-based predictions are processed in accordance with an unstructured fusion model to generate one or more unstructured-fused predictions.