Linhai Xie

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.

MEGA-ODE reconstructs dynamic omics landscapes from sparse data, predicts unmeasured states, and prioritizes perturbations toward biological endpoints.

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

PertDiffBench benchmarks diffusion models for predicting transcriptional responses to perturbations in single-cell and bulk RNA-seq data, showing mixed performance across different tasks and settings.

SciCore-Omics is a tri-modal foundation model that integrates histology, spatial transcriptomics, and language to improve spatial biology analysis and interpretation.

ChiMER detects enhancer-exon fusion transcripts by integrating chromatin architecture into splicing graphs, improving sensitivity over conventional methods.

quantms-rescoring improves proteomics analysis by integrating multiple search engines and machine learning, increasing identified spectra and quantifying more phosphorylated peptides and modification sites.

The paper introduces an automated system for high-throughput proteomic data generation, demonstrating its robustness and application in spatial proteomics and plasma/cell line profiling.

This paper introduces SRPS, a subtyping algorithm that improves classification accuracy and prognostic discrimination in multicentric proteomic studies, identifying PPIC as a significant biomarker in HCC.

The paper introduces Cell Decoder, a deep learning model that provides multi-scale interpretability for cell type decoding and data integration, showing its effectiveness in human and mouse datasets.

DeepRTAlign improves retention time alignment for large cohort LC-MS data, enhancing identification sensitivity without compromising quantitative accuracy and enabling predictive modeling for hepatocellular carcinoma recurrence.

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.