Christian A. Choe

This paper demonstrates that deep learning can accurately design RNA pseudoknots, achieving results comparable to human experts in a competition.

ADAPT-M rapidly quantifies protein binder affinities and kinetics using microfluidics, enabling efficient selection of high-affinity variants from yeast-display libraries.

The paper designs compact RNA sensors for complex functions of multiple inputs using crowdsourced designs and an algorithm, achieving applications like tuberculosis diagnosis.

RNAGym provides a benchmarking framework for RNA fitness, secondary structure, and tertiary structure prediction using diverse datasets and standardized assays.

The paper introduces RibonanzaNet, a deep learning model trained on crowdsourced RNA chemical mapping data to predict RNA structure and related properties.

The paper introduces Ig-VAE, a variational auto-encoder that generates 3D coordinates of immunoglobulin proteins, demonstrating its use in creating a SARS-CoV2-RBD binder and guiding protein design.

This study develops PERSIST-seq and In-line-seq to optimize mRNA stability and translation, finding that in-cell stability is more critical than ribosome load for protein output.