International Business Machines Corporation

Globalfoundries U.S. 2 LLC

William Scott Spangler

Jeffrey Thomas Kreulen

Ying Chen

Sheng Hua Bao

Xiaoyang Gao

James J Rhodes

Brian Scott Dreher

Feng Wang

Yanyan Han

Dharmendra S Modha

Bin He

Meenakshi Nagarajan

Vikas Krishna

Hovey Raymond Strong

Jacques Labrie

Alix M Lacoste

Qi He

Richard L Martin

Justin Thomas Lessler

Olivier Lichtarge

Angela D Wilkins

Lawrence A Donehower

Benjamin J Bachman

Hrishikesh Sathe

Sam J Regenbogen

Curtis R Pickering

Shivakumar Vaithyanathan

Linda Ha Kato

David Charles Martin

Eugene Michael Maximilien

Sumit Bhatia

Stephen Kane Boyer

Ana Lelescu

Katherine Shen

Su Yan

William Cody

Thilina Gunarathne

Karen Mae Holland

Li Ni Zhang

Rui Ma

Achille Belly Fokoue-Nkoutche

Keke Cai

Ping Zhang

Michael D Pfeifer

Matthias Eichstaedt

Cartic Ramakrishnan

Ioana Roxana Stanoi

Mark Earl Plutowski

Hovey Ramond Strong, Jr

Amit Behal

Sanjoy Dey

Ignacio Gabriel Terrizzano

Pathirage D Perera

Hyung-il Ahn

Daniel Thornton Pierce

Larry Lee Proctor

David Martinez Iraola

Henry Haitian Chen

Michael Arthur Lamb

Xin Jin

Kevin R Wheeler

David Hilton Wolpert

Anbu Karani Adikesavan

Dharmendra Shantilal Mohda

Maria E Terron-Diaz

Mohammad Hasan

Michael Ponce Sanchez

Hansel Joseph Miranda

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

Mechanisms are provided to implement a genomic database curation (GDC) system. The GDC system generates a ground truth database based on a training subset of datasets from an uncurated large scale genomic database, and label metadata for the training subset. The GDC system trains at least one classification engine of the GDC system based on the training subset and the ground truth database at least by performing a machine learning operation on the at least one classification engine. The GDC system automatically applies the at least one trained classification engine on the uncurated large scale genomic database to generate an automatically curated large scale genomic database. A meta-classifier engine generates an output specifying at least one of significant gene signatures or gene pathways for at least one of diseases or drug agents based on the automatically curated large scale genomic database.

Identifying gene signatures and corresponding biological pathways based on an automatically curated genomic database

According to embodiments of the present invention, methods, systems and computer readable media are provided, in a cognitive data processing system, for implementing a predictive analytics system that utilizes entity and non-entity information. A collection of content is processed to extract defined entities pertaining to one or more domains. Semantic relationships are determined between objects within the collection of content, wherein the objects include undefined entities. The defined entities and objects are resolved based on entity definitions and the semantic relationships to determine defined entities and undefined entities for a resulting data set. The resulting data set is processed to identify one or more relationships between a defined entity and an undefined entity.

Combining semantic relationship information with entities and non-entities for predictive analytics in a cognitive system

According to embodiments of the present invention, similarity metrics or measures of similarity may be combined using an adaptive weighting scheme. A subset of entities from a first set of entities that have a known relationship is randomly selected. The subset is combined with a second set of entities that have an unknown relationship to each other and/or to the first set of entities. At least two different measures of similarity (similarity metrics) between the first set and the combined second set (including the subset) is determined for each entity in the second set. For each entity in the second set, the at least two different measures of similarity are compared, and a weight is assigned adaptively to each measure of similarity based on the magnitude of the measure of similarity. The weighted measures of similarity are combined to determine an aggregate adaptively weighted similarity score for each entity.

Adaptive weighting of similarity metrics for predictive analytics of a cognitive system

Methods, systems, and computer program products are provided for processing a request regarding relationships among instances of entities. A graphical representation of instances of entities is generated and includes one or more source nodes, each representing an instance of an input entity of a request, and one or more related nodes, each representing an instance of a second entity related to one or more corresponding instances of the input entity and associated with a corresponding confidence score for the relationship. Each of the one or more related nodes associated with a confidence score satisfying a threshold is identified. One or more supplemental nodes are added to the graphical representation, each of which represents a corresponding instance of a third entity with a relationship to a corresponding instance of the second entity. The graphical representation is traversed to identify relationships between instances of entities and produce results for the request.

Searching for and determining relationships among entities

Methods, systems and computer readable media are provided for accessing data utilizing a multi-level table comprising generating a plurality of levels of the multi-level table, wherein a first level of the multi-level table includes a hyper-table with a plurality of hyper-cells each hyper-cell including information for a group of cells from an initial base table, wherein intermediate levels of the multi-level table each include a plurality of hyper-tables comprising hyper-cells with each hyper-table linked to and providing information for a corresponding hyper-cell of a hyper-table of a prior level, and wherein a plurality of tables of a terminal level includes information from cells of the initial base table with each table linked to and providing information for a corresponding hyper-cell. Data from the multi-level table is accessed by traversing links between the hyper-tables of the plurality of levels to access data within the tables of the terminal level.

Accessing data in a multi-level display for large data sets

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

Methods, systems and computer readable media are provided for accessing faceted information using ontologies. Information for an initial entity, including different ontologies to which the initial entity belongs, is retrieved. Entities within different ontologies are determined. The determined entities and different ontologies are displayed on a user interface to enable traversal of the different ontologies for viewing of the determined entities. Accordingly, a user may search for an initial entity. Different ontologies, linked to the initial entity may be returned. A user may select an ontology, and ontologies in which the initial entity belongs are displayed. The user may select any of the displayed ontologies to access other entities in the selected ontology.

Accessing faceted information using ontologies

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.

Rationalizing network predictions using similarity to known connections

According to embodiments of the present invention, an entity may be represented by an unstructured feature vector comprising a plurality of features extracted from unstructured data using semantic analysis and a structural feature vector comprising a plurality of features from chemical structure data. A similarity matrix may be used to compare entities and generate a similarity score, based on both the unstructured feature vector and the structural feature vector for each entity. In some aspects, a user may enter a query (from which a chemical structural feature vector is dynamically generated) to compare against entities having unstructured and/or structural feature vectors, stored in a database.

Combining chemical structure data with unstructured data for predictive analytics in a cognitive system

A method for ranking a relationship between drugs and adverse events includes creating a matrix of associations between a plurality of drugs and a plurality of adverse events and factoring the matrix of associates into a pair of matrix factors. The matrix factors, when multiplied, approximate the matrix of associations, and a product of the matrix factors is a matrix of observed scores. The method further includes determining, for each drug and adverse event pair in the matrix of observed scores, a z-score, an expected score for each drug and adverse event pair, and a standard deviation for each drug and adverse event pair, calculating a probability of a relationship between a drug and adverse event using the z-score for the drug and adverse event pair, and determining that the drug and adverse event are related, when the probability of a relationship is greater than a predetermined magnitude.

Estimating the probability of matrix factorization results

Growing community of inventors

San Martin, CA, United States of America

William Scott Spangler