Renzhi Cao

The paper introduces RNA3D-Decoy, a benchmark dataset of AI-generated RNA structure models, to evaluate quality assessment methods for RNA structure prediction.

ContrastQA improves protein complex structure quality assessment using label-guided graph contrastive learning, achieving lower ranking losses than existing methods on CASP16 datasets.

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

GOBoost improves protein function prediction by addressing long-tail distribution of Gene Ontology Terms using an ensemble strategy and specialized loss functions.

AnglesRefine refines 3D protein structures using a Transformer model focused on torsion angles, improving model quality and reducing computational time.

This paper introduces DeepTracer-Refine, an automated method to refine AlphaFold2 protein structures, improving accuracy and efficiency compared to existing methods.

The paper introduces DeepTracer-2.0, an AI pipeline that automatically models protein-DNA/RNA complexes from cryo-EM maps, improving accuracy and efficiency in macromolecular structure prediction.

ZoomQA evaluates residue-level accuracy of protein structure and complex predictions using sequential and 3D structural features without homology searches.

This paper introduces SynthQA, a hierarchical machine learning method for protein model quality assessment that outperforms traditional methods by incorporating multi-scale features and feature relationships.

Cascaded CNNs segmenting secondary structure, backbone and Cα voxels, plus tabu-search tracing, place 88.5% of Cα atoms within 3Å across 50 cryo-EM maps versus Phenix's 66.8%.

This paper explores AI methods, including reinforcement learning, for protein structure prediction and discusses their implications for bioinformatics and biotechnology.

The paper describes improvements to the MULTICOM system for protein structure prediction using deep learning and contact distance prediction in CASP13, achieving top rankings in both template-free and template-based modeling.

The paper develops deep learning models to predict both local and global quality of protein structural models, showing comparable performance to existing methods.

The paper introduces AngularQA, an LSTM-based method for protein model quality assessment using a novel representation of atom information, achieving a correlation of 0.651 on CASP12.