Yu Wang

π-SPECFormer: A Mass Spectrometry-based Foundation Model for Decoding the Dark Proteome

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.

DNA-SaM, a robust system for large-scale data storage

DNA-SaM improves DNA data storage by achieving high efficiency, scalability, and safety through advanced encoding and architecture.

E2VD: a unified evolution-driven framework for virus variation drivers prediction

E2VD predicts virus variation drivers using an evolution-driven framework, outperforming existing methods and identifying critical mutations for viral survival and risk.

Running ahead of evolution - AI based simulation for predicting future high-risk SARS-CoV-2 variants

This paper develops an AI-based simulation method to predict future high-risk SARS-CoV-2 variants by simulating RBD mutations and screening potential variants efficiently.

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.

Self-supervised learning on millions of pre-mRNA sequences improves sequence-based RNA splicing prediction

This study introduces SpliceBERT, a self-supervised pre-trained language model for RNA splicing prediction, showing improved performance in variant effect prediction and cross-species splice site identification.

Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning

LMetalSite predicts metal ion-binding sites from protein sequences using a pretrained language model and multi-task learning, achieving higher accuracy than structure-based methods.

ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing

This paper trains and evaluates language models on protein sequences to predict structural and localization features, achieving state-of-the-art results without evolutionary information.

Modeling the Language of Life - Deep Learning Protein Sequences

This paper introduces SeqVec, a method to represent protein sequences as continuous vectors using deep learning, achieving better prediction results than methods relying on evolutionary information.