Tianze Ling

This paper introduces π-SPECFormer, a foundation model for mass spectrometry-based proteomics that decodes the dark proteome by pre-training on unannotated spectra and outperforms baselines in peptide sequencing and modification detection.

The paper introduces π-MSNet, a large-scale proteomics dataset for AI training and benchmarking, showing performance improvements when models are retrained on it.

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.

This paper introduces a method to control false discovery rates in deep-learning-based de novo peptide sequencing, improving reliability of peptide identifications.

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.

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.

The paper introduces RNALID, a database of 358 RNA-ligand interactions validated by low-throughput experiments, showing novel ligands and structural patterns with implications for drug discovery.