Boyan Sun

π-MNovo improves de novo peptide sequencing through microbial-domain adaptation and evidence-guided candidate selection

π-MNovo improves de novo peptide sequencing for metaproteomics by adapting to microbial spectra and selecting candidates based on evidence, achieving higher recall than existing models.

PhyloFunc: Phylogeny-informed Functional Distance as a New Ecological Metric for Metaproteomic Data Analysis

The paper introduces PhyloFunc, a new functional beta-diversity metric for metaproteomic data that incorporates phylogenetic relationships to better capture functional dissimilarities between microbial communities.

π-PrimeNovo: An Accurate and Efficient Non-Autoregressive Deep Learning Model for De Novo Peptide Sequencing

π-PrimeNovo is a non-autoregressive deep learning model for de novo peptide sequencing that achieves higher accuracy and 69x faster performance than existing methods.

Introducing π-HelixNovo for practical large-scale de novo peptide sequencing

The paper introduces π-HelixNovo, a Transformer-based model for de novo peptide sequencing that outperforms existing methods and improves taxonomic resolution in gut metaproteomes.