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

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

Provided herein are methods and systems for making patient-specific therapy recommendations of a combination of any two or more therapies selected from a lipid-lowering therapy, an anti-inflammatory therapy for patients with known or suspected cardiovascular disease, such as atherosclerosis.

Gene sequences are tailored for protein expression by measuring ribosome dynamics, training a statistical model of the relationship between DNA sequence and translation speed; and using this model to design an optimal DNA sequence encoding a given protein.

Gene sequences are tailored for protein expression by measuring ribosome dynamics, training a statistical model of the relationship between DNA sequence and translation speed; and using this model to design an optimal DNA sequence encoding a given protein.

A method and system for analyzing a target network relative to a background network of data using machine learning. The method includes extracting a first feature matrix from an adjacency matrix representative of the target network, extracting a second feature matrix from an adjacency matrix representative of the background network, generating a projection matrix based on the first and second feature matrices using a contrastive learning algorithm, generating a first contrastive matrix representation of the target network based on the projection matrix and the first feature matrix, generating a second contrastive matrix representation of the background network based on the projection matrix and the second feature matrix, and displaying a visualization of unique features of the target network relative to the background network based on the first contrastive matrix and the second contrastive matrix.

A method and system for analyzing a target network relative to a background network of data using machine learning. The method includes extracting a first feature matrix from an adjacency matrix representative of the target network, extracting a second feature matrix from an adjacency matrix representative of the background network, generating a projection matrix based on the first and second feature matrices using a contrastive learning algorithm, generating a first contrastive matrix representation of the target network based on the projection matrix and the first feature matrix, generating a second contrastive matrix representation of the background network based on the projection matrix and the second feature matrix, and displaying a visualization of unique features of the target network relative to the background network based on the first contrastive matrix and the second contrastive matrix.

Described in certain example embodiments herein are systems, methods, and uses thereof for high-throughput in vitro evaluating multiple test compounds in parallel for biological or pharmacological functions. In certain embodiments, the system allows the selection of a subset of test compounds from a group of test compounds to form an optimized pool, and methods are provided to use such optimized pool of test compounds to identify and validate therapeutic agents for treating diseases and driving guided differentiation of stem cells into desired types of cells. The systems described herein can provide, for example, a cost-effective and high-quality high-throughput approach for drug screening.

Described in certain example embodiments herein are systems, methods, and uses thereof for high-throughput in vitro evaluating multiple test compounds in parallel for biological or pharmacological functions. In certain embodiments, the system allows the selection of a subset of test compounds from a group of test compounds to form an optimized pool, and methods are provided to use such optimized pool of test compounds to identify and validate therapeutic agents for treating diseases and driving guided differentiation of stem cells into desired types of cells. The systems described herein can provide, for example, a cost-effective and high-quality high-throughput approach for drug screening.

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.