LATENT VARIABLE MODELING TO SEPARATE PCR BIAS AND BINDING AFFINITY

    公开(公告)号:US20210158890A1

    公开(公告)日:2021-05-27

    申请号:US16692522

    申请日:2019-11-22

    Abstract: The present disclosure relates to development of aptamers, and in particular to developing machine-learning models to describe characteristics of a given sequence for an aptamer and based on the characteristics find other sequences for aptamers not observed experimentally, and techniques for separating out sequences for aptamers that are present primarily due to PCR bias and/or binding affinity. Particularly, aspects of the present disclosure are directed to obtaining sequence data for an aptamer sequence that binds to a target, generating a binding affinity latent variable and a PCR bias latent variable based on the sequence data, generating a predicted count of the aptamer sequence based on the binding affinity latent variable and PCR bias latent variable, determining that the binding affinity latent variable is greater than the PCR bias latent variable, and in response to the determining, accepting the predicted count of the aptamer sequence.

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