Abstract: In embodiments of latent space harmonization (LSH) for predictive modeling, different training data sets are obtained from different measurement methods, where input data among the training data sets is quantifiable in a common space but a mapping between output data among the training data sets is unknown. A LSH module receives the training data sets and maps a common supervised target variable of the output data to a shared latent space where the output data can be jointly yielded. Mappings from the shared latent space back to the output training data of each training data set are determined and used to generate a trained predictive model. The trained predictive model is useable to predict output data from new input data with improved predictive power from the training data obtained using various, otherwise incongruent, measurement techniques.

Abstract: In embodiments of latent space harmonization (LSH) for predictive modeling, different training data sets are obtained from different measurement methods, where input data among the training data sets is quantifiable in a common space but a mapping between output data among the training data sets is unknown. A LSH module receives the training data sets and maps a common supervised target variable of the output data to a shared latent space where the output data can be jointly yielded. Mappings from the shared latent space back to the output training data of each training data set are determined and used to generate a trained predictive model. The trained predictive model is useable to predict output data from new input data with improved predictive power from the training data obtained using various, otherwise incongruent, measurement techniques.

Abstract: This disclosure presents a model for identifying correlations in genome-wide association studies (GWAS) with function-valued traits that provides increased power and computational efficiency by use of a Gaussian process regression with radial basis function (RBF) kernels to model the function-valued traits and specialized factorizations to achieve speed. A Gaussian Process is assigned to each partition for each allele of a given single nucleotide polymorphism (SNP) which yields flexible alternative models and handles a large number of data points in a way that is statistically and computationally efficient. This model provides techniques for handling missing and unaligned function values such as would occur when not all individuals are measured at the same time points. If the data is complete algebraic re-factorization by decomposition into Kronecker products reduces the time complexity of this model thereby increasing processing speed and reducing memory usage as compared to a naive implementation.

Abstract: In embodiments of latent space harmonization (LSH) for predictive modeling, different training data sets are obtained from different measurement methods, where input data among the training data sets is quantifiable in a common space but a mapping between output data among the training data sets is unknown. A LSH module receives the training data sets and maps a common supervised target variable of the output data to a shared latent space where the output data can be jointly yielded. Mappings from the shared latent space back to the output training data of each training data set are determined and used to generate a trained predictive model. The trained predictive model is useable to predict output data from new input data with improved predictive power from the training data obtained using various, otherwise incongruent, measurement techniques.

Abstract: This disclosure presents a model for identifying correlations in genome-wide association studies (GWAS) with function-valued traits that provides increased power and computational efficiency by use of a Gaussian process regression with radial basis function (RBF) kernels to model the function-valued traits and specialized factorizations to achieve speed. A Gaussian Process is assigned to each partition for each allele of a given single nucleotide polymorphism (SNP) which yields flexible alternative models and handles a large number of data points in a way that is statistically and computationally efficient. This model provides techniques for handling missing and unaligned function values such as would occur when not all individuals are measured at the same time points. If the data is complete algebraic re-factorization by decomposition into Kronecker products reduces the time complexity of this model thereby increasing processing speed and reducing memory usage as compared to a naive implementation.

Abstract: A control system comprises an input configured to receive sensor data sensed from a target system to be controlled by the control system. The control system has an input-aware stacker, the input-aware stacker being a predictor; and a plurality of base predictors configured to compute base outputs from features of the sensor data. The input-aware stacker is input-aware in that it is configured to take as input the features as well as the base outputs to compute a prediction. The input-aware stacker is configured to compute the prediction from uncertainty data about the base outputs and/or from at least some combinations of the features of the sensor data. The control system has an output configured to send instructions to the target system on the basis of the computed prediction.

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: 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: 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: 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 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: 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.