Patents by Inventor Katherine Shen
Katherine Shen has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240085417Abstract: Provided are methods of assigning a LAG+, LAG?, or PRO immunotype to a cancer patient based on the frequencies of LAG-3+CD8+T-cells, Ki67+CD8+T-cells, Tim-3+CD8+T-cells, and ICOS+CD8+T-cells in a peripheral blood sample from the patient, and selecting an anti-cancer therapy, for example, an immune checkpoint blockade (ICB) therapy, based on the patient's immunotype.Type: ApplicationFiled: January 20, 2022Publication date: March 14, 2024Inventors: Margaret K. Callahan, Arshi Arora, Taha Merghoub, Katherine S. Panageas, Michael A. Postow, Ronglai Shen, Jedd D. Wolchok
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Patent number: 11651235Abstract: 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.Type: GrantFiled: November 28, 2018Date of Patent: May 16, 2023Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: William S. Spangler, Alix Lacoste, Katherine Shen, Hrishikesh Sathe, Jacques Labrie
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Patent number: 11276494Abstract: 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.Type: GrantFiled: May 11, 2018Date of Patent: March 15, 2022Assignee: International Business Machines CorporationInventors: Ping Zhang, Achille B Fokoue-Nkoutche, Sanjoy Dey, Katherine Shen
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Patent number: 11120914Abstract: 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.Type: GrantFiled: November 2, 2018Date of Patent: September 14, 2021Assignee: International Business Machines CorporationInventors: Sanjoy Dey, Achille B. Fokoue-Nkoutche, Katherine Shen, Ping Zhang
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Patent number: 11120913Abstract: 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.Type: GrantFiled: January 24, 2018Date of Patent: September 14, 2021Assignee: International Business Machines CorporationInventors: Sanjoy Dey, Achille B. Fokoue-Nkoutche, Katherine Shen, Ping Zhang
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Patent number: 11055380Abstract: 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.Type: GrantFiled: November 9, 2018Date of Patent: July 6, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: William Scott Spangler, Katherine Shen
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Patent number: 11011254Abstract: A method, computer system, and a computer program product for identifying and storing at least one representation to at least one chemical compound is provided. The present invention may include identifying a chemical compound associated with a source data. The present invention may also include assigning a structure representation to the identified chemical compound associated with the source data. The present invention may further include computing an unformulated representation based on the assigned structure representation. The present invention may then include indexing the computed unformulated representation and the assigned structure representation. The present invention may further include storing the indexed unformulated representation and the indexed structure representation separately as single records in a database.Type: GrantFiled: July 31, 2018Date of Patent: May 18, 2021Assignee: International Business Machines CorporationInventors: Richard L. Martin, Katherine Shen
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Patent number: 10902943Abstract: 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.Type: GrantFiled: May 17, 2018Date of Patent: January 26, 2021Assignee: International Business Machines CorporationInventors: Ping Zhang, Achille B. Fokoue-Nkoutche, Sanjoy Dey, Katherine Shen
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Publication number: 20200167663Abstract: 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.Type: ApplicationFiled: November 28, 2018Publication date: May 28, 2020Inventors: William S. Spangler, Alix Lacoste, Katherine Shen, Hrishikesh Sathe, Jacques Labrie
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Publication number: 20200151301Abstract: 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.Type: ApplicationFiled: November 9, 2018Publication date: May 14, 2020Inventors: WILLIAM SCOTT SPANGLER, KATHERINE SHEN
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Publication number: 20200042671Abstract: A method, computer system, and a computer program product for identifying and storing at least one representation to at least one chemical compound is provided. The present invention may include identifying a chemical compound associated with a source data. The present invention may also include assigning a structure representation to the identified chemical compound associated with the source data. The present invention may further include computing an unformulated representation based on the assigned structure representation. The present invention may then include indexing the computed unformulated representation and the assigned structure representation. The present invention may further include storing the indexed unformulated representation and the indexed structure representation separately as single records in a database.Type: ApplicationFiled: July 31, 2018Publication date: February 6, 2020Inventors: Richard L. Martin, Katherine Shen
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Publication number: 20190355458Abstract: 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.Type: ApplicationFiled: May 17, 2018Publication date: November 21, 2019Inventors: Ping Zhang, Achille B. Fokoue-Nkoutche, Sanjoy Dey, Katherine Shen
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Publication number: 20190348179Abstract: 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.Type: ApplicationFiled: May 11, 2018Publication date: November 14, 2019Inventors: Ping Zhang, Achille B. Fokoue-Nkoutche, Sanjoy Dey, Katherine Shen
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Publication number: 20190228864Abstract: 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.Type: ApplicationFiled: January 24, 2018Publication date: July 25, 2019Inventors: Sanjoy Dey, Achille B. Fokoue-Nkoutche, Katherine Shen, Ping Zhang
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Publication number: 20190228865Abstract: 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.Type: ApplicationFiled: November 2, 2018Publication date: July 25, 2019Inventors: Sanjoy Dey, Achille B. Fokoue-Nkoutche, Katherine Shen, Ping Zhang