Michelle J. Wu

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

A crowdsourced RNA design approach discovers efficient, reversible molecular sensors that couple small molecule inputs to protein binding and fluorogenic outputs.

This paper introduces a convolutional neural network to model nucleic acid motif energies, showing it can accurately predict thermodynamics of unmeasured motifs.

This paper evaluates a recurrent neural network model, Ribonet, for learning RNA biophysics from high-throughput data, showing it can predict RNA interactions and design functional RNAs but requires more diverse datasets for accurate biophysical modeling.