Haodong Wang

This paper introduces DeepUMQA-Selection, a framework for efficiently selecting high-quality protein complex models from large candidate pools, showing improved performance and speed.

This paper introduces DeepUMQA-Global, a deep learning framework for estimating protein structure model accuracy, which outperforms existing methods in correlation metrics and consensus rankings.

MViewEMA improves protein complex model accuracy estimation with multi-view representation learning, achieving higher efficiency and performance than previous methods.

This paper introduces DeepUMQA-PA, a deep learning method to estimate residue-wise quality scores for protein complex models, showing improvements over existing methods on benchmark datasets.

The paper introduces multiMiAT, a microbiome-based association test for multicategory phenotypes, showing it performs well in simulations and real data analyses.