International Business Machines Corporation

Roland Mathis

Mitra Purandare

Matteo Manica

Maria Rodriguez Martinez

Konstantinos Bekas

Raphael C Polig

Computer-implemented methods are provided for generating a personalized Boolean model for a genetic disease of a patient. The method includes storing specification data and reference model data. The reference model includes gene nodes, representing genes, connected to Boolean circuitry and a plurality of inputs for receiving binary input values. Each gene node in the reference model comprises a multiplexer. The multiplexer has a first input and an output, a second input for receiving a binary mutation value, and a control input for receiving a binary selector value. The method further comprises using a model checker to determine if the specification is reachable in the reference model. If the specification is reachable, the method includes identifying each multiplexer whose second input was connected to its output in the path reaching the specification to obtain mutation data for the patient, generating a personalized Boolean model, and outputting personal model data.

Genetic disease modeling

An example of an embodiment is directed to a computer-implemented method for providing a cloud service to execute a computing task of a model specification. The method includes receiving, by the cloud service, the model specification and input data for the model specification from a user. The method further includes generating, by the cloud service, native code from the model specification and executing, by the cloud service, the computing task by executing the native code as a native process with the input data. The method also includes providing, by the cloud service, results of the computing task to the user. Other embodiments further concern a related computing system and a related computer program product.

Native code generation for cloud services

The present disclosure relates to a system and method for extracting information from text data. The method comprises: obtaining a plurality of text elements. A word embedding algorithm may be applied to the obtained text elements by mapping each text element of at least part of the text elements into a vector of a predefined dimension. The mapped text elements may be clustered into groups using the distances between the respective vectors. For each text element of a set of text elements of the mapped text elements a respective distribution of neighbors across the groups may be built. Similarity scores may be computed using the distributions thereby for determining relations between the set of text elements.

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Zurich, Switzerland

Roland Mathis