International Business Machines Corporation

Alix M Lacoste

William Scott Spangler

Feng Wang

Achille Belly Fokoue-Nkoutche

Meenakshi Nagarajan

Christine Schieber

Jacques Labrie

Richard L Martin

Antonio Jose Jimeno Yepes

Katherine Shen

David Martinez Iraola

Sundar Saranathan

Hrishikesh Sathe

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.

Generating a candidate set of entities from a training set

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.

Data curation on predictive data modelling platform

According to an embodiment of the present invention, a system designates each document in a collection of documents as a member of a first group containing known subjects for a concept of interest or as a member of a second group containing candidate subjects for the concept of interest and determines a subset of documents for at least one subject. The system generates a classifier based on the documents in the first and second groups and applies the classifier to a set of documents for the at least one subject to determine whether each document belong to the first and/or second group. The system generates a score for the at least one subject based on a quantity of documents for that subject assigned to the first group of documents relative to a total quantity of documents for that subject and ranks that subject based on the determined score for each subject.

Machine learning for ranking candidate subjects based on a training set

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.

Extraction and normalization of mutant genes from unstructured text for cognitive search and analytics

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. N hypotheses correlating the pharmaceutical vector and one or more of the plurality of disease vectors are generated, each hypothesis indicating a potential repurposing for the pharmaceutical to treat the disease.

Accelerated pharmaceutical repurposing by finding anticorrelations and by text mining

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.

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New York, NY, United States of America

Alix M Lacoste