PREDICTING DISEASE OUTCOMES USING MACHINE LEARNED MODELS

Embodiments of the disclosure include implementing a ML-enabled cellular disease model for validating an intervention, identifying patient populations that are likely responders to an intervention, and developing a therapeutic structure-activity relationship screen. To generate a cellular disease mo...

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Hauptverfasser: SALICK, Max, R, KOLLER, Daphne, KAYKAS, Ajamete, STANITSAS, Panagiotis Dimitrios, SHARON, Eilon, RIESSELMAN, Adam, Joseph, KATEGAYA, Lorn, COTTA-RAMUSINO, Cecilia, Giovanna, Silvia, CASALE, Francesco Paolo, SULTAN, Mohammad Muneeb, PALMEDO, Peter, Franklin, Jr
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creator SALICK, Max, R
KOLLER, Daphne
KAYKAS, Ajamete
STANITSAS, Panagiotis Dimitrios
SHARON, Eilon
RIESSELMAN, Adam, Joseph
KATEGAYA, Lorn
COTTA-RAMUSINO, Cecilia, Giovanna, Silvia
CASALE, Francesco Paolo
SULTAN, Mohammad Muneeb
PALMEDO, Peter, Franklin, Jr
description Embodiments of the disclosure include implementing a ML-enabled cellular disease model for validating an intervention, identifying patient populations that are likely responders to an intervention, and developing a therapeutic structure-activity relationship screen. To generate a cellular disease model, data is combined from human genetic cohorts, from the literature, and from general-purpose cellular or tissue-level genomic data to unravel the set of factors (e.g., genetic, environmental, cellular factors) that give rise to a particular disease. In vitro cells are engineered using the set of factors to generate training data for training machine learning models that are useful for implementing cellular disease models.
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language eng ; fre ; ger
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subjects BEER
BIOCHEMISTRY
CHEMISTRY
COMPOSITIONS OR TEST PAPERS THEREFOR
CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL ORENZYMOLOGICAL PROCESSES
ENZYMOLOGY
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEICACIDS OR MICROORGANISMS
METALLURGY
MICROBIOLOGY
MUTATION OR GENETIC ENGINEERING
PHYSICS
PROCESSES OF PREPARING SUCH COMPOSITIONS
SPIRITS
VINEGAR
WINE
title PREDICTING DISEASE OUTCOMES USING MACHINE LEARNED MODELS
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