IMPLEMENTING A GENERATIVE MACHINE LEARNING ARCHITECTURE TO PRODUCE TRAINING DATA FOR A CLASSIFICATION MODEL

Amino acid sequences of proteins can be produced using one or more generative machine learning architectures. The amino acid sequences produced by the one or more generative machine learning architectures can be used to train a classification model architecture. The classification model architecture...

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Bibliographic Details
Main Authors Ketchem, Randal Robert, Shaver, Jeremy Martin, Amimeur, Tileli, Smith, Joshua
Format Patent
LanguageEnglish
Published 10.08.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
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Summary:Amino acid sequences of proteins can be produced using one or more generative machine learning architectures. The amino acid sequences produced by the one or more generative machine learning architectures can be used to train a classification model architecture. The classification model architecture can classify amino acid sequences according to a number of classifications. Individual classifications of the number of classifications can correspond to at least one of a structural feature of proteins, a range of values of a structural feature of proteins, a biophysical property of proteins, or a range of values of a biophysical property of proteins.
Bibliography:Application Number: US202118043528