Abstract: Systems and methods for managing progression of a clinical trial. Input data for a machine learning model is formed, based on longitudinal data for clinical trial cohort. The input data corresponds to input features and the cohort includes a plurality of subjects. A clinical outcome output is generated for each subject, using the machine learning model and a portion of the input data corresponding to each subject. Feature importance values are generated, based on the machine learning model generating the clinical outcome output for each subject. The feature importance values include, for each subject, a set of feature importance values for a set of input features. A ratio of interest is computed using the plurality of feature importance values. An output is generated using the ratio of interest in which the output indicates whether the cohort should proceed to a next phase of the clinical trial.

Abstract: A system for interface automation includes an agent. The agent is configured to process an input that specifies an interface workflow, wherein the interface workflow is otherwise implementable by one or more user-actuated actions directed towards an interface by a user. The agent is also configured to generate an output that specifies a sequence of actuation commands, wherein the sequence of actuation commands triggers one or more machine-actuated actions that replicate the user-actuated actions on the interface and cause automation of the interface workflow.

Abstract: A method and system for predicting a set of linking parameters that relate pharmacokinetic and pharmacodynamic effects. One or more processors receive a population dataset that comprises a population pharmacokinetic (PK) dataset and a population pharmacodynamic (PD) dataset. The one or more processors transform the population dataset into a plurality of data density images that includes a PK data density image and a PD data density image. The one or more processors predict the set of linking parameters using the plurality of data density images.

Abstract: A system for providing artificial intelligence agents that automate software usage includes training servers configured to train agents during training, production servers configured to execute the trained agents during inference, a plurality of training datasets, and data flow logic. The data flow logic is configured to, provide, during the training, the agents and the plurality of training datasets to the training servers to cause the training servers to train the agents on the plurality of training datasets and thereby produce the trained agents, configure the production servers with the trained agents for use during the inference, provide, during the inference, prompts issued by clients to the production servers to cause the production servers to translate the prompts into agent calls to the trained agents that in turn cause the trained agents to generate outputs that are responsive to the prompts, and make the outputs available to the clients.

Abstract: Artificial Intelligence Agents to Automate Multimodal Interface Task Workflows A system for interface automation includes an agent. The agent is configured to process an input that specifies an interface workflow, wherein the interface workflow is otherwise implementable by one or more user-actuated actions directed towards an interface by a user. The agent is also configured to generate an output that specifies a sequence of actuation commands, wherein the sequence of actuation commands triggers one or more machine-actuated actions that replicate the user-actuated actions on the interface and cause automation of the interface workflow.

Abstract: A method and system for predicting at least one pharmacokinetic parameter of an agent administered to a subject. One or more processors train, by one or more processors, a neural network based on a simulated training data collection. The simulated training data collection comprising a simulated time-series concentration dataset and a simulated value for a pharmacokinetic parameter that corresponds to the simulated time-series concentration dataset. The one or more processors receive a time-series concentration dataset of the agent obtained from a subject. The one or more processors predict a value for the pharmacokinetic parameter using the time-series concentration dataset and the neural network that has been trained.

Abstract: A method may include training a machine learning model to determine a first pharmacokinetic parameter and a second pharmacokinetic parameter for a molecule. The machine learning model may be trained by at least determining, based at least on an input including one or more pharmacokinetic models associated with the molecule, a first value of the first pharmacokinetic parameter, determining, based at least on the input, a second value of the first pharmacokinetic parameter, and determining, based at least on the first value and the second value of the first pharmacokinetic parameter, a third value of the second pharmacokinetic parameter. The method may also include applying the trained machine learning model to determine, based at least on a sparsely sampled pharmacokinetic model associated with the molecule, the first pharmacokinetic parameter and/or the second pharmacokinetic parameter. Related methods and articles of manufacture are also disclosed.

Abstract: A method and system for evaluating a treatment using one or more model parameters associated with a quantitative systems pharmacology (QSP) system. Input data corresponding to a treatment is received. The input data is sent to a machine learning system that has been trained, the machine learning system representing at least a portion of the QSP model. The machine learning system is used to generate a set of values for a set of treatment effect parameters associated with the QSP model. A final output is generated based on an evaluation of the treatment on a subject using the set of values for the set of treatment effect parameters.

Abstract: A semiconductor device structure may include a substrate having a substrate base comprising a first dopant type; a semiconductor layer disposed on a surface of the substrate base, the semiconductor layer comprising a second dopant type and having an upper surface; and a semiconductor plug assembly comprising a semiconductor plug disposed within the semiconductor layer, the semiconductor plug extending from an upper surface of the semiconductor layer and having a depth at least equal to a thickness of the semiconductor layer, the semiconductor plug having a first boundary, the first boundary formed within the semiconductor layer, and having a second boundary, the second boundary formed within the semiconductor layer and disposed opposite the first boundary, wherein the first boundary and second boundary extend perpendicularly to the surface of the substrate base.

Abstract: A method and system for predicting at least one pharmacokinetic parameter of an agent administered to a subject. One or more processors train, by one or more processors, a neural network based on a simulated training data collection. The simulated training data collection comprising a simulated time-series concentration dataset and a simulated value for a pharmacokinetic parameter that corresponds to the simulated time-series concentration dataset. The one or more processors receive a time-series concentration dataset of the agent obtained from a subject. The one or more processors predict a value for the pharmacokinetic parameter using the time-series concentration dataset and the neural network that has been trained.

Abstract: A method and system for predicting a set of linking parameters that relate pharmacokinetic and pharmacodynamic effects. One or more processors receive a population dataset that comprises a population pharmacokinetic (PK) dataset and a population pharmacodynamic (PD) dataset. The one or more processors transform the population dataset into a plurality of data density images that includes a PK data density image and a PD data density image. The one or more processors predict the set of linking parameters using the plurality of data density images.

Abstract: A method for predicting pharmacokinetic-pharmacodynamic effects over time is provided. A pharmacokinetic pathway of a neural network system that lies at least partially within an ordinary differential equations (ODE) module of the neural network system is trained to generate a dose effect output associated with a drug. A pharmacodynamic pathway of the neural network system that lies at least partially within the ODE module is trained to generate a drug effect output associated with the drug. The drug effect output associated with an administration of the drug over a time period is predicted using the neural network system.

Abstract: A method for predicting a clinical outcome for a subject. Input data for a group of features is formed using baseline data and tumor kinetic data derived from longitudinal data for a set of variables for the subject, the set of variables including tumor size. A clinical outcome output that provides an indication of the clinical outcome for the subject is generated using a pan-indication model and the input data. The pan-indication model includes a gradient boosting decision tree-based ensemble machine learning algorithm.

Abstract: A semiconductor device structure may include a substrate having a substrate base comprising a first dopant type; a semiconductor layer disposed on a surface of the substrate base, the semiconductor layer comprising a second dopant type and having an upper surface; and a semiconductor plug assembly comprising a semiconductor plug disposed within the semiconductor layer, the semiconductor plug extending from an upper surface of the semiconductor layer and having a depth at least equal to a thickness of the semiconductor layer, the semiconductor plug having a first boundary, the first boundary formed within the semiconductor layer, and having a second boundary, the second boundary formed within the semiconductor layer and disposed opposite the first boundary, wherein the first boundary and second boundary extend perpendicularly to the surface of the substrate base.

Abstract: A semiconductor device structure may include a substrate having a substrate base comprising a first dopant type; a semiconductor layer disposed on a surface of the substrate base, the semiconductor layer comprising a second dopant type and having an upper surface; and a semiconductor plug assembly comprising a semiconductor plug disposed within the semiconductor layer, the semiconductor plug extending from an upper surface of the semiconductor layer and having a depth at least equal to a thickness of the semiconductor layer, the semiconductor plug having a first boundary, the first boundary formed within the semiconductor layer, and having a second boundary, the second boundary formed within the semiconductor layer and disposed opposite the first boundary, wherein the first boundary and second boundary extend perpendicularly to the surface of the substrate base.

Abstract: Systems, platforms, methods and media for providing genomic services are disclosed. In one example, a genomic services platform comprises a network interface through which are received genomic sequence reads derived from a biological sample obtained from a user The platform also includes a bioinformatics processing pipeline including a read alignment module configured to generate observed sequence data by aligning the sequence reads relative to a reference sequence, a variant calling module operative to identify observed variants in the observed sequence data, and a variant refinement module for producing a set of refined variants associated with the user. A variant imputation module produces a set of imputed variants associated with the user, and a variant storage module disposed to receive a query from network infrastructure of a partner application provider and to provide selected ones of the refined or imputed variants in response to the query.

Abstract: Systems and methods are provided for protected search. A search query to be performed using at least one data store is received from a computing device of a user. The user is not authorized to access the at least one data store. One or more search results that are responsive to the search query from the at least one data store are determined. The one or more search results are aggregated based on one or more categories. At least a subset of the aggregated search results is provided to the computing device of the user.

Abstract: A semiconductor device structure may include a substrate having a substrate base comprising a first dopant type; a semiconductor layer disposed on a surface of the substrate base, the semiconductor layer comprising a second dopant type and having an upper surface; and a semiconductor plug assembly comprising a semiconductor plug disposed within the semiconductor layer, the semiconductor plug extending from an upper surface of the semiconductor layer and having a depth at least equal to a thickness of the semiconductor layer, the semiconductor plug having a first boundary, the first boundary formed within the semiconductor layer, and having a second boundary, the second boundary formed within the semiconductor layer and disposed opposite the first boundary, wherein the first boundary and second boundary extend perpendicularly to the surface of the substrate base.

Abstract: Systems, platforms, methods and media for providing genomic services axe disclosed. In one example, a system for providing genomic services comprises genomic sequencing equipment configured to generate sequence reads based upon a biological sample obtained from a user, store the sequence reads in a FASTQ storage file, and communicate the FASTQ file electronically to a recipient. The system also includes a genomic services platform which includes a network interface through which the sequence reads are received, and a bioinformatics processing pipeline. The bioinformatics processing pipeline includes a read alignment module configured to generate observed sequence data, and a variant calling module is operative to identify observed variants in the observed sequence data. A variant storage module is disposed to receive a query from network infrastructure of a partner application provider and to provide selected ones of refined variants in response to a query.

Abstract: Systems, platforms, methods and media for providing genomic services are disclosed. In one example, a genomic services platform comprises a network interface through which are received genomic sequence reads derived from a biological sample obtained from a user. The platform also includes a bioinformatics processing pipeline including a read alignment module configured to generate observed sequence data by aligning the sequence reads relative to a reference sequence, a variant calling module operative to identify observed variants in the observed sequence data, and a variant refinement module for producing genotype data including a set of refined variants associated with the user. A variant imputation module produces a set of imputed variants associated with the user, and is configured to receive, as input, at least some of the genotype data and separate the genotype data into high-quality and low-quality genotypes based on a genotype quality.