International Business Machines Corporation

Ping Zhang

Jianying Hu

Sanjoy Dey

Fei Wang

Achille Belly Fokoue-Nkoutche

Robert K Sorrentino

Nan Cao

Zhaonan Sun

Katherine Shen

Jing Li

Ying Li

Mohammad Sadoghi Hamedani

Guo Tong Xie

Oktie Hassanzadeh

Meinolf Sellmann

Mohamed Ghalwash

Heng Luo

Robert G Farrell

William Scott Spangler

Gang Hu

Yi Qin Yu

Haifeng Liu

Hai Feng Liu

Gigi Yuen-Reed

Kun Lin

Zach Shahn

Yashu Liu

Techniques are described that facilitate determining potential cancer gene therapy targets by joint modeling of cancer survival events. In one embodiment, a computer-implemented comprises employing, by a device operatively coupled to a processor, a multi-task learning model to determine active genetic factors respectively associated with different types of cancer based on cancer survival data and patient genomic data for groups of patients that respectively survived the different types of cancer. The computer-implemented method further comprises, determining, by the device, common active genetic factors of the active genetic factors that are shared between two or more types of cancer of the different types of cancer.

Determining potential cancer therapeutic targets by joint modeling of survival events

Systems, computer-implemented methods and/or computer program products that facilitate providing treatment suggestions are described herein. In one example, a computer-implemented method comprises: generating, by a system operatively coupled to a processor, treatment directed graphs associated with a plurality of treatment regimens, wherein, in connection with generating respective treatment directed graphs, penalties are applied during weighting of respective regimens as a function of an associated confidence level; identifying a treatment directed graph that corresponds to the patient directed graph; and outputting a set of treatment suggestions associated with the treatment directed graph that corresponds to the patient directed graph.

Facilitating health intervention suggestion for disease mitigation and/or prevention

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.

Various embodiments predict drug-disease associations. In one embodiment, a plurality of disease similarity matrices and a plurality of disease similarity matrices are accessed. Each of the plurality of drug similarity matrices is associated with a different drug information source. Each of the plurality of disease similarity matrices is associated with a different disease information source. A known drug-disease association matrix is also accessed. The known drug-disease association matrix indicates if a given drug identified is known to treat a given disease. At least one drug-disease association prediction is generated based on the plurality of drug similarity matrices, the plurality of disease similarity matrices, and the known drug-disease association matrix. The at least one drug-disease association prediction identifies a previously unknown association between a given drug and a given disease, and a probability that the given disease is treatable by the given drug.

Generating drug repositioning hypotheses based on integrating multiple aspects of drug similarity and disease similarity

Predicting beneficial drug combinations mitigating adverse drug reactions identifies drug combinations and associated target adverse drug reaction from a spontaneous reporting system containing case reports of drugs and associated adverse drug reactions. Each drug combination comprises a first drug and a second drug, and a propensity score is computed for each drug in each group. This propensity score expresses a probability of being exposed to a given drug based on other co-prescribed drugs and reported indications, which reflect patient characteristics. Associations are computed for each drug as well as drug interaction. Among the associations, the sum of the associations of the second drug and the interaction effect represents the predicted beneficial score expressing whether the second drug alters the chance of developing the target adverse drug reaction for patients on the first drug. The interaction effect is referred to as predicted interaction score, and represents antagonistic or synergistic drug interactions.

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White Plains, NY, United States of America

Ping Zhang