Jie Hou

The paper introduces a large dataset of cryo-EM RNA motifs and benchmarks machine learning models for 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.

This study benchmarks graph neural networks for RNA secondary structure prediction, showing they outperform traditional energy-based methods.

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

This paper introduces sc-f GAIN, an f-divergence-based generative adversarial imputation method for scRNA-seq data that improves accuracy and robustness compared to traditional methods.

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.

The paper improves protein tertiary structure prediction by enhancing deep learning and distance prediction methods in the MULTICOM system, achieving better performance in CASP14.

The paper develops deep learning-based EMA predictors for protein model accuracy estimation in CASP14, achieving top rankings in GDT-TS score loss.

The paper develops deep learning-based protein distance predictors for CASP14, achieving top rankings in contact prediction and showing the importance of MSAs and features in accuracy.

This paper introduces DeepGRN, an attention-based model for transcription factor binding site prediction, showing improved performance on ENCODE-DREAM datasets.

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.

DeepDist predicts real-value inter-residue distances using a deep residual network, improving contact prediction and tertiary structure reconstruction compared to multi-class methods.

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%.

SAXSDom improves domain-domain assembly accuracy using SAXS data and IOHMM, achieving better results on CASP and AIDA datasets.

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.

DNSS2 improves protein secondary structure prediction using advanced deep learning architectures, achieving high accuracy on benchmark datasets.

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.

DNCON2 improves protein contact prediction using two-level deep convolutional networks, achieving higher precision than existing methods on CASP datasets.