Walter Reade

The paper describes a Kaggle challenge for particle picking in cryo-electron tomography, resulting in improved algorithms and highlighting the need for better annotation quality measures.

This work analyzes CAFA5 submissions, showing improved accuracy in protein function prediction and highlights the role of protein language models and 3D structures in performance gains.

The paper describes a Kaggle competition for RNA 3D structure prediction, showing that template-based methods can outperform deep learning approaches.

The paper introduces MotorBench, a dataset of bacterial flagellar motors for testing automated detection algorithms in cryo-ET.

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