Ronghui You

Protein language model powers accurate and fast sequence search for remote homology

PLMSearch uses a protein language model to perform fast and accurate homologous protein sequence search, improving sensitivity over traditional methods while matching structure search performance.

Effective structure-aware protein alignment via residue-level contrastive learning

CLAlign improves protein alignment by using contrastive learning to generate structure-aware embeddings, outperforming both unsupervised and sequence-based methods.

NetGO 3.0: Protein Language Model Improves Large-scale Functional Annotations

NetGO 3.0 integrates a protein language model with NetGO 2.0 to improve functional annotations by leveraging unannotated proteins.

MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities

MetaBinner improves metagenomic binning by integrating multiple features and SCG information, outperforming existing methods on simulated and real datasets.

DeepMHCII: A Novel Binding Core-Aware Deep InteractionModel for Accurate MHC II-peptide Binding Affinity Prediction

DeepMHCII improves MHC II-peptide binding affinity prediction by incorporating binding core-aware convolution layers, outperforming existing methods across multiple evaluation settings.

Critical Assessment of Metagenome Interpretation - the second round of challenges

The paper evaluates metagenomic software tools using realistic datasets, highlighting improvements in assembly and taxon profiling while identifying remaining challenges in virus/archaea detection and reproducibility.

BERTMeSH: Deep Contextual Representation Learning for Large-scale High-performance MeSH Indexing with Full Text

BERTMeSH improves MeSH indexing by using deep learning and full-text analysis, achieving higher accuracy and faster performance than existing methods.

NetGO: Improving Large-scale Protein Function Prediction with Massive Network Information

NetGO improves large-scale protein function prediction by integrating sequence and network information, outperforming existing methods on CAFA benchmark data.

GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank

GOLabeler improves sequence-based protein function prediction by integrating multiple classifiers in a learning-to-rank framework, showing better performance than existing methods.