International Business Machines Corporation

Sanjoy Dey

Ping Zhang

Achille Belly Fokoue-Nkoutche

Katherine Shen

Jianying Hu

Mohamed Ghalwash

Heng Luo

William Scott Spangler

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

Embodiments of the present invention are directed to a computer-implemented method for generating a framework for analyzing adverse drug reactions. A non-limiting example of the computer-implemented method includes receiving to a processor, a plurality of drug chemical structures. The non-limiting example also includes receiving, to the processor, a plurality of known drug-adverse drug reaction associations. The non-limiting example also includes constructing, by the processor, a deep learning framework for each of a plurality of adverse drug reactions based at least in part upon the plurality of drug chemical structures and the plurality of known adverse-drug reaction associations.

Identifying chemical substructures associated with adverse drug reactions

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.

Predicting interactions between drugs and diseases

Mechanisms are provided for implementing a framework to learn multiple drug-adverse drug reaction associations. The mechanisms receive and analyze patient electronic medical record data and adverse drug reaction data to identify co-occurrences of references to drugs with references to adverse drug reactions (ADRs) to thereby generate candidate rules specifying multiple drug-ADR relationships. The mechanisms filter the candidate rules to remove a subset of one or more rules having confounder drugs specified in the subset of one or more candidate rules, and thereby generate a filtered set of candidate rules. The mechanisms further generate a causal model based on the filtered set of candidate rules. The causal model comprises, for each ADR in a set of ADRs, a corresponding set of one or more rules, each rule specifying a combination of drugs having a causal relationship with the ADR.

Finding precise causal multi-drug-drug interactions for adverse drug reaction analysis

A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a drug response estimation engine. The drug response estimation engine receives real-world evidence for a plurality of patients. A patient similarity network builder component executing within the drug response estimation engine builds a patient similarity network. A regression analysis component executing within the drug response estimation engine builds a network localized regression analysis approach. A patient clustering component executing within the drug response estimation engine groups patients based on demographics and comorbidities to form a plurality of patient clusters. The drug response estimation engine estimates drug responses for a given patient based on the patient similarity network, the network localized regression analysis approach, and the plurality of patient clusters. The drug response estimation engine outputs the drug responses for the given patient.

Estimating personalized drug responses from real world evidence

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.

Evaluating drug-adverse event causality based on an integration of heterogeneous drug safety causality models

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.

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.

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White Plains, NY, United States of America

Sanjoy Dey