Abstract: A method, apparatus and computer program product for establishing a logical connection between an indirect utterance and a transaction is described. An indirect utterance is received from a user as an input to a conversational system. The indirect utterance is parsed to a first logical form. A first set of predicates and terms is mapped from the first logical form to a first subgraph in a knowledge graph. A second set of predicates and terms is mapped from a second logical form belonging to a transaction to a second subgraph of the knowledge graph. A best path in the knowledge graph between the first subgraph and the second subgraph is searched for while transforming the first logical form using the node and edge labels along the best path to generate an intermediate logical form. A system action is performed for a transaction if a graph structure of the intermediate logical form matches the graph structure of the logical form of the transaction above a threshold.

Abstract: 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.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for predicting drug and disease interactions. A computer identifies one or more drug similarity measures between one or more drugs and one or more disease similarity measures between one or more diseases. In addition, the computer identifies one or more interactions between the one or more drugs and the one or more diseases, then calculates one or more drug-disease feature vectors based on the one or more interactions, the one or more drug similarity measures, and the one or more disease similarity measures. Furthermore, the computer calculates a first probability indicating whether a first drug of the one or more drugs will interact with a first disease of the one or more diseases based on a model, wherein the model is trained based on the one or more drug-disease feature vectors.

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 determining a composite similarity metric for data of a first data type is provided. The present invention may include providing a plurality of similarity metrics for the first data type. The present invention may also include providing a metric quantifying correlation of entities belonging to the first data type and entities belonging to a second data type. The present invention may then include developing a first regression model to predict values of the provided metric quantifying correlation of entities belonging to the first data type and entities belonging to the second data type using the provided plurality of similarity metrics. The present invention may further include calculating the composite similarity metric from a plurality of first regression coefficients in the developed first regression model.

Abstract: Mechanisms are provided that implement a plurality of heterogeneous causality models and a metaclassifier for predicting a likelihood of causality between a drug and an adverse event (AE). The plurality of heterogenous causality models process drug information for the drug to generate a plurality of risk predictions for a drug and AE pair. The risk predictions include at least one of a risk score or a risk label indicating a probability of the AE occurring with use of the drug. The plurality of heterogenous causality models provide the risk predictions, associated with the drug and AE pair, to a metaclassifier which generates a single causality score value indicative of a probability of causality between the drug and the AE, of the drug and AE pair, based on an aggregation of the risk predictions from the plurality of heterogenous causality models. The metaclassifier outputs the single causality score value in association with information identifying the drug and AE pair.

Abstract: Mechanisms are provided that implement a plurality of heterogeneous causality models and a metaclassifier for predicting a likelihood of causality between a drug and an adverse event (AE). The plurality of heterogenous causality models process drug information to generate a plurality of risk predictions for a drug and AE pair. The risk predictions include a risk score or a risk label indicating a probability of the AE occurring with use of the drug. The plurality of heterogenous causality models provide risk predictions, associated with the drug and AE pair, to a metaclassifier which generates a single causality score value indicative of a probability of causality between the drug and the AE, of the drug and AE pair, based on an aggregation of the risk predictions from the plurality of heterogenous causality models. The metaclassifier outputs the single causality score value in association with information identifying the drug and AE pair.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for predicting drug and food interactions. A computer identifies one or more drug similarity measures between one or more drugs and one or more food similarity measures between one or more foods. In addition, the computer identifies one or more interactions between the one or more drugs and the one or more foods, then calculates one or more drug-food feature vectors based on the one or more interactions, the one or more drug similarity measures, and the one or more food similarity measures. Furthermore, the computer calculates a first probability indicating whether a first drug of the one or more drugs will interact with a first food of the one or more foods based on a model, wherein the model is trained based on the one or more drug-food feature vectors.

Abstract: A knowledge graph is traversed by receiving a knowledge graph at a deep neural network, the knowledge graph including a plurality of nodes connected by a plurality of edges, each respective edge of the plurality of edges being associated with a corresponding distance representing embedded semantic information. The deep neural network is trained to capture the embedded semantic information. A path query is received at the deep neural network. A context is determined for the received path query at the deep neural network. The deep neural network performs the traversing of the knowledge graph in response to the received path query, based upon the determined context and the embedded semantic information.

Abstract: A method, apparatus and computer program product for responding to an indirect utterance in a dialogue between a user and a conversational system is described. An indirect utterance is received. A parse structure of the indirect utterance is generated. The indirect utterance is an utterance which does not match a user goal expressed as elements of a knowledge graph. The parse structure is connected through the knowledge graph to a user goal to issue a user request which is not stated in the indirect utterance. The parse structure is connected using a matching process which matches the parse structure with the connected user goal in the knowledge graph according to a similarity of the parse structure and a portion of the knowledge graph including the connected user goal. A system response is performed based on the connected user goal.

Abstract: Embodiments include method, systems and computer program products for predicting adverse drug events on a computational system. Aspects include receiving a personalized data set including a plurality of real-time drug doses for a first drug or drug combination and a plurality of corresponding real-time adverse drug reaction tolerance data for the first drug or drug combination for a patient. Aspects also include receiving known drug data for a candidate drug or drug pair. Aspects also include calculating, based upon the known drug data and the personalized data set, a predicted adverse drug reaction tolerance for the candidate drug or drug pair at a candidate dosage, wherein the predicted adverse drug reaction tolerance is personalized to the patient.

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: 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.

Abstract: A method, apparatus and computer program product for responding to an indirect utterance in a dialogue between a user and a conversational system is described. An indirect utterance is received. A parse structure of the indirect utterance is generated. The indirect utterance is an utterance which does not match a user goal expressed as elements of a knowledge graph. The parse structure is connected through the knowledge graph to a user goal to issue a user request which is not stated in the indirect utterance. The parse structure is connected using a matching process which matches the parse structure with the connected user goal in the knowledge graph according to a similarity of the parse structure and a portion of the knowledge graph including the connected user goal. A system response is performed based on the connected user goal.

Abstract: A method, apparatus and computer program product for responding to an indirect utterance in a dialog between a user and a conversational system is described. An indirect utterance is received. A parse structure of the indirect utterance is generated. The indirect utterance is an utterance which does not match a user goal expressed as elements of a knowledge graph. The parse structure is connected through the knowledge graph to a user goal to issue a request which is not stated in the indirect utterance. A system response is performed, where the system response is a dialog system response based on a combination of the parse structure and the connected user goal.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for predicting drug and food interactions. A computer identifies one or more drug similarity measures between one or more drugs and one or more food similarity measures between one or more foods. In addition, the computer identifies one or more interactions between the one or more drugs and the one or more foods, then calculates one or more drug-food feature vectors based on the one or more interactions, the one or more drug similarity measures, and the one or more food similarity measures. Furthermore, the computer calculates a first probability indicating whether a first drug of the one or more drugs will interact with a first food of the one or more foods based on a model, wherein the model is trained based on the one or more drug-food feature vectors.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for predicting drug and disease interactions. A computer identifies one or more drug similarity measures between one or more drugs and one or more disease similarity measures between one or more diseases. In addition, the computer identifies one or more interactions between the one or more drugs and the one or more diseases, then calculates one or more drug-disease feature vectors based on the one or more interactions, the one or more drug similarity measures, and the one or more disease similarity measures. Furthermore, the computer calculates a first probability indicating whether a first drug of the one or more drugs will interact with a first disease of the one or more diseases based on a model, wherein the model is trained based on the one or more drug-disease feature vectors.

Abstract: A method, computer system, and a computer program product for estimating error in predictions from a data model is provided. The present invention may include providing at least one first metric quantifying similarity of entities belonging to a first data type. The present invention may also include providing a second metric quantifying correlation of entities belonging to the first data type and entities belonging to a second data type. The present invention may then include developing a first model for predicting the second metric based on the at least one first metric. The present invention may further include developing a second model to estimate error in the first model.

Abstract: A method, computer system, and a computer program product for analyzing data belonging to a plurality of data types wherein data belonging to a first data type of the plurality of data types may be correlated with data belonging to a second data type of the plurality of data types is provided. The present invention may include providing at least one first metric quantifying similarity of entities belonging to the first data type. The present invention may then include providing a second metric quantifying correlation of entities belonging to the first data type and entities belonging to the second data type. The present invention may also include inferring a value of the second metric correlating a first entity of the first data type with a second entity of the second data type.

Abstract: A method, computer system, and a computer program product for determining a composite similarity metric for data of a first data type is provided. The present invention may include providing a plurality of similarity metrics for the first data type. The present invention may also include providing a metric quantifying correlation of entities belonging to the first data type and entities belonging to a second data type. The present invention may then include developing a first regression model to predict values of the provided metric quantifying correlation of entities belonging to the first data type and entities belonging to the second data type using the provided plurality of similarity metrics. The present invention may further include calculating the composite similarity metric from a plurality of first regression coefficients in the developed first regression model.