A process is provided for using a model to identify potential combinations of drugs to repair an action potential. A model is created for the normal state of an action potential along with a model for an abnormal state that reflects the effect of a disease or mutation on the action potential. Using a known action potential, a treated state can be generated that represents the abnormal state modified by one or more drug effects. By optimizing a formulation for a drug combination to minimize the differences between the treated state model and the normal state model, a combination of drug therapies that can potentially repair an action potential can be identified.

Provided herein are methods and systems for making patient-specific therapy recommendations of a lipid-lowering therapy for patients with known or suspected cardiovascular disease, such as atherosclerosis.

Systems and methods to assess metabolic dysregulation are described. Metabolic dysregulation refers to elevated glycemia or insulin resistance. The systems and methods assess metabolic dysregulation by determining which subphenotypes or underlying pathologies are contributing to the metabolic dysregulation. In some instances, a trained computational model utilizes an individual's glucose time series curve to determine the contribution of various metabolic dysregulation subphenotypes to the individual's metabolic dysregulation. Various applications or treatments can be performed based on the determination of metabolic dysregulation subphenotypes.

Systems and methods to assess metabolic dysregulation are described. Metabolic dysregulation refers to elevated glycemia or insulin resistance. The systems and methods assess metabolic dysregulation by determining which subphenotypes or underlying pathologies are contributing to the metabolic dysregulation. In some instances, a trained computational model utilizes an individual's glucose time series curve to determine the contribution of various metabolic dysregulation subphenotypes to the individual's metabolic dysregulation. Various applications or treatments can be performed based on the determination of metabolic dysregulation subphenotypes.

Provided herein are methods and systems for making patient-specific therapy recommendations of a lipid-lowering therapy for a patient with known or suspected atherosclerotic cardiovascular disease, such as atherosclerosis.

The present invention comprises the capture and display of arthropod, human and arthropod-based metadata, which is capable of tracking and displaying the metadata, which is time and location-based, in order to show migration paths of arthropods and/or the diseases they have the potential to carry. This real-time view can help predict future arthropod and disease based on various scenarios such as, but not limited to: increased exposure based on the following: a user's geo-location, date and/or time of year, carrier type, etc. These variables can then assist with the education, awareness and potential prevention of disease.

The present invention comprises the capture and display of arthropod, human and arthropod-based metadata, which is capable of tracking and displaying the metadata, which is time and location-based, in order to show migration paths of arthropods and/or the diseases they have the potential to carry. This real-time view can help predict future arthropod and disease based on various scenarios such as, but not limited to: increased exposure based on the following: a user's geo-location, date and/or time of year, carrier type, etc. These variables can then assist with the education, awareness and potential prevention of disease.

The present disclosure relates to a machine learning method and apparatus using steps feature selection based on a genetic algorithm, and the machine learning method includes defining a feature set including a plurality of features, generating a plurality of feature combinations including n-dimensional features (n is a natural number) for the feature set, independently constructing feature models for the plurality of feature combinations and calculating prediction accuracy for each of the feature models as a prediction result for a predetermined data set, arranging the feature models according to the prediction accuracy to determine at least one good feature model that satisfies a preset criterion, determining at least one good feature from among features included in a corresponding feature set of the at least one good feature model, and updating the feature set to include only the at least one good feature and re-determining a good feature model for a (n+1)-dimensional feature combination based on the updated feature set.

The present disclosure relates to a machine learning method and apparatus using steps feature selection based on a genetic algorithm, and the machine learning method includes defining a feature set including a plurality of features, generating a plurality of feature combinations including n-dimensional features (n is a natural number) for the feature set, independently constructing feature models for the plurality of feature combinations and calculating prediction accuracy for each of the feature models as a prediction result for a predetermined data set, arranging the feature models according to the prediction accuracy to determine at least one good feature model that satisfies a preset criterion, determining at least one good feature from among features included in a corresponding feature set of the at least one good feature model, and updating the feature set to include only the at least one good feature and re-determining a good feature model for a (n+1)-dimensional feature combination based on the updated feature set.

Systems and methods for associating cellular constituents with a cellular process of interest are provided. Constituent vectors comprising abundances for a first plurality of cells representing annotated cell states are formed and used to obtain a latent representation of constituent modules having subsets of constituents. A constituent count data structure comprising abundances of the constituents for a second plurality of cells representing covariates of interest is obtained. An activation data structure is formed by combining the latent representation and the constituent count data structure, using constituents as a common dimension. A model is trained using a difference between the predicted and actual absence or presence of each covariate in each cellular constituent module represented in the activation data structure, thus adjusting covariate weights indicating a correlation between covariates and constituent modules across the activation data structure. The covariate weights are used to identify constituent modules associated with covariates of interest.