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Found 3 papers, 0 papers with code
Using Interpretable Machine Learning to Massively Increase the Number of Antibody-Virus Interactions Across Studies
We use this framework to greatly expand seven influenza datasets utilizing hemagglutination inhibition, validating our method upon 200, 000 existing measurements and predicting 2, 000, 000 new values along with their uncertainties.
When Two are Better than One: Modeling the Mechanisms of Antibody Mixtures
It is difficult to predict how antibodies will behave when mixed together, even after each has been independently characterized.
How the Avidity of Polymerase Binding to the -35/-10 Promoter Sites Affects Gene Expression
We show that this the refined energy matrix model can characterize the full suite of gene expression data and explore several applications of this framework, namely, how multivalent binding at the -35 and -10 sites can buffer RNAP kinetics against mutations and how promoters that bind overly tightly to RNA polymerase can inhibit gene expression.
