A novel AI-driven drug-repurposing method, DeepDrug, is used to identify a lead combination of previously FDA-approved drugs to treat AD by targeting the upstream genetic markers along the AD pathology. A three-step methodology is used. First, a heterogeneous biomedical graph is constructed comprising complex and interconnected genes, proteins, and drug information to capture the network characteristics of the AD pathology, considering the expert known associations between different AD pathways and utilizing node weighting and edge weighting and direction. Second, the curated graph is taken as an input to an artificial intelligence (AI)-driven graphical neural network (GNN) framework, with embeddings of drug and gene nodes as the outputs. Third, a drug scoring and selection analysis is conduced to generate the drug-gene scores and identify a lead combination of repurposed AD drug candidates for clinical verification.

A novel AI-driven drug-repurposing method, DeepDrug, is used to identify a lead combination of previously FDA-approved drugs to treat AD by targeting the upstream genetic markers along the AD pathology. A three-step methodology is used. First, a heterogeneous biomedical graph is constructed comprising complex and interconnected genes, proteins, and drug information to capture the network characteristics of the AD pathology, considering the expert known associations between different AD pathways and utilizing node weighting and edge weighting and direction. Second, the curated graph is taken as an input to an artificial intelligence (AI)-driven graphical neural network (GNN) framework, with embeddings of drug and gene nodes as the outputs. Third, a drug scoring and selection analysis is conduced to generate the drug-gene scores and identify a lead combination of repurposed AD drug candidates for clinical verification.

The present disclosure discloses a method for establishing a medicine synergism prediction model, a prediction method and corresponding apparatus, and relates to deep learning and artificial intelligence (AI) medical technologies in the field of AI technologies. A specific implementation solution includes: acquiring a relation graph, nodes in the relation graph including medicine nodes and protein nodes, and edges indicating that interaction exists between the nodes; collecting, from the relation graph, a medicine node pair with definite synergism and a label of whether the medicine node pair has synergism as training samples; and training the medicine synergism prediction model by taking the medicine node pair in the training samples as input to the medicine synergism prediction model and taking the label of whether the medicine node pair has synergism as target output; wherein the medicine synergism prediction model is obtained by learning the relation graph based on a graph convolutional network.

An image analysis device that ease association between an SAR image and an object is provided. The image analysis device includes: a stable reflection point identification unit that identifies, based on a plurality of synthetic aperture radar (SAR) images, stable reflection points at which reflection is stable in the plurality of SAR images; a phase identification unit that identifies a phase at each of the stable reflection points, based on the plurality of SAR images and a location of the stable reflection point in the plurality of SAR images; and a clustering means that clusters the stable reflection points, based on a Euclidian distance between each of the stable reflection points and a correlation of the phases at each of the stable reflection points.

This invention relates to a method for users localization and guidance of the movements of the users through a building adapted in relation to an event, along paths leading to one or more given target places, with connected objects (T, T′) in communication link with a remote resource server which is a control server (S) accessible by a communication network, comprising the following steps: on the basis of a digital model of the building (BIM, CIM), nodes (N) and edges (A) are computed; a user waiting list is assigned to each passage node; a Directed Acyclic Graph (DAG) of movement toward the target places (“E”, “S”, “W”) is automatically computed with the nodes and the edges (A); the actual location of an event is detected; the actual position of each user located in the building is computed; as a function of the profile of the user, of the user's location in real time in the building causing the inaccessibility of certain nodes of the Directed Acyclic Graph (DAG), the so-called ‘updated’ Directed Acyclic Graph (DAG′) of moving toward the target places; and a sub-graph DODAG′ are computed.

Systems and methods for modeling higher-level metrics from graph data derived from already-collected but not yet connected data are disclosed. A method includes extracting a first set of actor-related data, a second set of object-related data, and a third set of temporal data from the set of the already-collected but not yet connected data representative of a unit-level contribution to the target activity. The method further includes generating graph data for a graph using the set of the already-collected but not yet connected data, where each of the plurality of nodes of the graph corresponds to the actor or the object, and where an attribute associated with each of the plurality of edges of the graph corresponds to a measurement associated with the target activity. The method further includes modeling a relationship between graph attributes associated with the graph data and a higher-level metric associated with the target activity.

Systems and methods for providing a consolidated transfer ecosystem are provided. Systems may include a graph database. The graph database may include a plurality of nodes representing a plurality of entities. The system may receive a plurality of transfer requests and represent each transfer request as an edge on the graph database. The system may include a consolidation engine that may consolidate the edges in the graph database to produce a consolidated database. The system may execute the transfers according to the consolidated database.

Systems and methods for providing a consolidated transfer ecosystem are provided. Systems may include a graph database. The graph database may include a plurality of nodes representing a plurality of entities. The system may receive a plurality of transfer requests and represent each transfer request as an edge on the graph database. The system may include a consolidation engine that may consolidate the edges in the graph database to produce a consolidated database. The system may execute the transfers according to the consolidated database.

In an approach to CRAI and risk framework for manufacturing applications, there is thus provided a computer-implemented method for causal effect prediction, the computer-implemented method including: identifying, by one or more computer processors, an intervention, wherein the intervention is selected from the group consisting of threats, failures, corrections, and relevant outputs; collecting, by the one or more computer processors, process dependency data; creating, by the one or more computer processors, an intervention model; combining, by the one or more computer processors, the process dependency data and the intervention model to create a combined process dependency graph; training, by the one or more computer processors, a causal relational artificial intelligence (CRAI) model; and determining, by the one or more computer processors, an estimate of an intervention efficacy.

The present disclosure is directed to systems and methods for classifying biomedical images. A feature classifier may generate a plurality of tiles from a biomedical image. Each tile may correspond to a portion of the biomedical image. The feature classifier may select a subset of tiles from the plurality of tiles by applying an inference model. The subset of tiles may have highest scores. Each score may indicate a likelihood that the corresponding tile includes a feature indicative of the presence of the condition. The feature classifier may determine a classification result for the biomedical image by applying an aggregation model. The classification result may indicate whether the biomedical includes the presence or lack of the condition.