Abstract: A method, computer system, and a computer program product for generating a candidate set of entities from a training set of entities is provided. The present invention may include determining an ontology class for an input entity in the training set of entities. The present invention may include adding the input entity to an ontology list. The present invention may then include assigning an entity score to the input entity. The present invention may also include normalizing the ontology list of entity scores. The present invention may lastly include selecting the candidate set of entities with the highest entity score.

Abstract: A method, system, and computer program product for a data modelling platform to engage with a user via a user interface is provided. A predictive data model is displayed based on the user query. Provenance and evidence is provided based on a user selected result. The ground truth dataset is modified in response to receiving a user action via the user interface.

Abstract: A method, computer system, and a computer program product for automatically extracting and normalizing at least one mutant gene entity from at least one set of unstructured text is provided. The present invention may include extracting the unstructured text describing first and second entities. The present invention may then include identifying a specific first entity and a specific second entity. The present invention may also include associating the specific first and the specific second entities. The present invention may further include creating the mutant gene entity. The present invention may then include identifying at least one semantic relationship between the created mutant gene entity and one or more third entities. The present invention may further include storing the at least one set of data associated with the specific first and specific second entity, the semantic relationship, and the created mutant gene entity in a database.

Abstract: Utilizing a computing device to assist in repurposing of a pharmaceutical. An identification of a pharmaceutical for repurposing study is received by a computing device. A pharmaceutical expression signature is retrieved based upon the identification of the pharmaceutical, the pharmaceutical expression signature indicating differential expressions of a plurality of biomolecules regulated by the pharmaceutical. A plurality of disease expression signatures are retrieved from a disease omics database, each disease expression signature indicating differential expressions of a plurality of biomolecules affected by a disease. A pharmaceutical vector is generated based upon the pharmaceutical expression signature for the pharmaceutical. A plurality of disease vectors are generated based upon the plurality of disease expression signatures for each disease.

Abstract: Rationalization of network predictions using similarity to known connections is provided. In various embodiments, a graph is read. The graph comprises a plurality of nodes. Each of the plurality of nodes corresponds to an entity or property. The plurality of nodes is interconnected by a plurality of edges. Each edge corresponds to a relationship between connected nodes. A new edge in the graph is predicted. The new edge corresponds to a relationship between a first node and a second node. The first node corresponds to an entity and the second node corresponds to an entity or property. One or more additional nodes connected to the second node is located. The one or more additional nodes is scored according to its connections in common with the first node. One or more sources is provided to a user describing the connection between the one or more additional node and the second node.

Abstract: Methods and apparatus are provided for generating a graph neural network (GNN) model based on an entity-entity graph. The entity-entity graph comprising a plurality of entity nodes in which each entity node is connected to one or more entity nodes of the plurality of entity nodes by one or more corresponding relationship edges. The method comprising: generating an embedding based on data representative of the entity-entity graph for the GNN model, wherein the embedding comprises an attention weight assigned to each relationship edge of the entity-entity graph; and updating weights of the GNN model including the attention weights by minimising a loss function associated with at least the embedding; wherein the attention weights indicate the relevancy of each relationship edge between entity nodes of the entity-entity graph. The entity-entity graph may be filtered based on the attention weights of a trained GNN model.

Abstract: Rationalization of network predictions using similarity to known connections is provided. In various embodiments, a graph is read. The graph comprises a plurality of nodes. Each of the plurality of nodes corresponds to an entity or property. The plurality of nodes is interconnected by a plurality of edges. Each edge corresponds to a relationship between connected nodes. A new edge in the graph is predicted. The new edge corresponds to a relationship between a first node and a second node. The first node corresponds to an entity and the second node corresponds to an entity or property. One or more additional nodes connected to the second node is located. The one or more additional nodes is scored according to its connections in common with the first node. One or more sources is provided to a user describing the connection between the one or more additional node and the second node.

Abstract: A method, system, and computer program product for a data modelling platform to engage with a user via a user interface is provided. A predictive data model is displayed based on the user query. Provenance and evidence is provided based on a user selected result. The ground truth dataset is modified in response to receiving a user action via the user interface.

Abstract: A method, computer system, and a computer program product for generating a candidate set of entities from a training set of entities is provided. The present invention may include determining an ontology class for an input entity in the training set of entities. The present invention may include adding the input entity to an ontology list. The present invention may then include assigning an entity score to the input entity. The present invention may also include normalizing the ontology list of entity scores. The present invention may lastly include selecting the candidate set of entities with the highest entity score.

Abstract: Techniques for analysis of relationship consistency are provided. A plurality of relationships is extracted from a plurality of documents, and a binary matrix is generated based on the plurality of relationships. A first relationship, of the plurality of relationships, is identified to be verified. A score of the first relationship in the binary matrix is set to a predefined value. Further, a factorization is performed on the binary matrix to produce a first matrix and a second matrix. A first consistency score is calculated for the first relationship by multiplying at least a portion of the first matrix and a second matrix. The first consistency score is ranked as compared to at least one other consistency score associated with at least one other relationship of the plurality of relationships. Finally, an indication of the first relationship is provided, based on the ranking.

Abstract: Rationalization of network predictions using similarity to known connections is provided. In various embodiments, a graph is read. The graph comprises a plurality of nodes. Each of the plurality of nodes corresponds to an entity or property. The plurality of nodes is interconnected by a plurality of edges. Each edge corresponds to a relationship between connected nodes. A new edge in the graph is predicted. The new edge corresponds to a relationship between a first node and a second node. The first node corresponds to an entity and the second node corresponds to an entity or property. One or more additional nodes connected to the second node is located. The one or more additional nodes is scored according to its connections in common with the first node. One or more sources is provided to a user describing the connection between the one or more additional node and the second node.

Abstract: A method, computer system, and a computer program product for automatically extracting and normalizing at least one mutant gene entity from at least one set of unstructured text is provided. The present invention may include extracting the unstructured text describing first and second entities. The present invention may then include identifying a specific first entity and a specific second entity. The present invention may also include associating the specific first and the specific second entities. The present invention may further include creating the mutant gene entity. The present invention may then include identifying at least one semantic relationship between the created mutant gene entity and one or more third entities. The present invention may further include storing the at least one set of data associated with the specific first and specific second entity, the semantic relationship, and the created mutant gene entity in a database.

Abstract: Utilizing a computing device to assist in repurposing of a pharmaceutical. An identification of a pharmaceutical for repurposing study is received by a computing device. A pharmaceutical expression signature is retrieved based upon the identification of the pharmaceutical, the pharmaceutical expression signature indicating differential expressions of a plurality of biomolecules regulated by the pharmaceutical. A plurality of disease expression signatures are retrieved from a disease omics database, each disease expression signature indicating differential expressions of a plurality of biomolecules affected by a disease. A pharmaceutical vector is generated based upon the pharmaceutical expression signature for the pharmaceutical. A plurality of disease vectors are generated based upon the plurality of disease expression signatures for each disease.

Abstract: Rationalization of network predictions using similarity to known connections is provided. In various embodiments, a graph is read. The graph comprises a plurality of nodes. Each of the plurality of nodes corresponds to an entity or property. The plurality of nodes is interconnected by a plurality of edges. Each edge corresponds to a relationship between connected nodes. A new edge in the graph is predicted. The new edge corresponds to a relationship between a first node and a second node. The first node corresponds to an entity and the second node corresponds to an entity or property. One or more additional nodes connected to the second node is located. The one or more additional nodes is scored according to its connections in common with the first node. One or more sources is provided to a user describing the connection between the one or more additional node and the second node.