G16B40/00

COMPUTATIONAL SYSTEMS AND METHODS FOR IMPROVING THE ACCURACY OF DRUG TOXICITY PREDICTIONS

In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

COMPUTATIONAL SYSTEMS AND METHODS FOR IMPROVING THE ACCURACY OF DRUG TOXICITY PREDICTIONS

In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

Quantitative DNA-based imaging and super-resolution imaging

The present disclosure provides, inter alia, methods and compositions (e.g., conjugates) for imaging, at high spatial resolution, targets of interest.

Quantitative DNA-based imaging and super-resolution imaging

The present disclosure provides, inter alia, methods and compositions (e.g., conjugates) for imaging, at high spatial resolution, targets of interest.

Discovering population structure from patterns of identity-by-descent

Described are techniques for determining population structure from identity-by-descent (IBD) of individuals. The techniques may be used to predict that an individual belongs to zero, one or more of a number of communities identified within an IBD network. Additional data may be used to annotate the communities with birth location, surname, and ethnicity information. In turn, these data may be used to provide to an individual a prediction of membership to zero, one or more communities, accompanied by a summary of the information annotated to those communities.

Discovering population structure from patterns of identity-by-descent

Described are techniques for determining population structure from identity-by-descent (IBD) of individuals. The techniques may be used to predict that an individual belongs to zero, one or more of a number of communities identified within an IBD network. Additional data may be used to annotate the communities with birth location, surname, and ethnicity information. In turn, these data may be used to provide to an individual a prediction of membership to zero, one or more communities, accompanied by a summary of the information annotated to those communities.

Optimization of gene sequences for protein expression

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.

Optimization of gene sequences for protein expression

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.

System and method for contrastive network analysis and visualization

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.

System and method for contrastive network analysis and visualization

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.