Tiannan Guo

The paper introduces Plasmix, a plasma reference suite, to improve cross-platform proteomics integration by identifying proteins with consistent titration responses across platforms.

The paper introduces microProteomEx, a platform combining tissue expansion, imaging, and proteomics to achieve high-resolution spatial proteomics in hard tissues and clinical samples.

Drug-Prot quantifies drug effects and interactions using proteomic data, revealing protein-level causal relationships and actionable drug combinations.

The paper presents a comprehensive spatial proteome map of human tissues and cancers, enabling drug response analysis and target discovery.

This study uses population-based metaproteomics to identify functional associations between gut microbiota and 39 phenotypes, including 1,381 features related to Type 2 Diabetes.

This study evaluates blood contamination in nanoparticle-based plasma proteomics, identifies contamination sources, and introduces software to detect and mitigate contamination in clinical samples.

The paper creates a spatially-resolved human brain proteome atlas, revealing protein diversity and functional specialization, and identifies therapeutic targets in epilepsy.

MassNet provides a large-scale mass spectral corpus for proteomics, enabling AI-driven de novo peptide sequencing with improved performance over existing methods.

DIA-BERT improves DIA proteomics analysis by using pre-trained transformer models, achieving higher protein and peptide identifications than DIA-NN in cancer samples.

ProteoAutoNet improves CF-MS analysis by integrating a multi-database search strategy with robotics, enhancing throughput and identifying novel protein interactions in thyroid cancer.

The paper develops a foundation model using perturbation proteomics data to predict drug responses and improve breast cancer prognosis.

Integral-Oms enables sequential extraction and profiling of multiple omics layers from biopsy tissue, revealing suppressed ferroptosis in colorectal cancer tissues.

DDA-BERT improves PSM rescoring in proteomics using a transformer-based model trained on 95 million human spectra, outperforming existing methods across species and sample types.

metaExpertPro constructs metaproteomics spectral libraries and analyzes DIA-MS data, achieving high peptide quantification and low FDR in human fecal samples.

MetaDIA reduces the search space for DIA gut metaproteomics by prioritizing peptides based on species and functional abundance, achieving results consistent with traditional DDA-based methods.

The paper introduces FAXP, a method combining hydrogel expansion and mass spectrometry for high-resolution proteomics of FFPE tissues, showing improved resolution and efficiency over existing methods.

The paper introduces a database called COVIDpro that curates proteomic data from COVID-19 patients and provides tools for analyzing protein dysregulation to identify biomarkers and therapeutic targets.

BiATNovo improves de novo peptide sequencing from DIA mass spectrometry data by using attention-based bidirectional models and post-processing, achieving better accuracy for long peptides and amino acid-level predictions.

DPHL v2 improves upon DPHL v1 by including semi-tryptic peptides and protein isoforms, increasing identified and dysregulated proteins in colorectal cancer analysis.

Dear-DIAXMBD is a deep autoencoder method for analyzing DIA proteomics data, achieving superior performance across different species and instrument platforms.

This study identifies a 24-protein panel distinguishing TNBC from other breast cancers and shows proteomics improves drug sensitivity predictions.

The paper constructs a comprehensive mass spectral library for human thyroid tissues to improve the detection of malignant nodules through proteomic analysis.

BatchServer provides a web platform for evaluating, visualizing, and correcting batch effects in omics data using autoComBat, PVCA, and UMAP.

The paper investigates how genomic alterations affect molecular networks in prostate cancer by integrating genomic, transcriptomic, and proteomic data from 105 samples, identifying a sub-network predictive of tumor aggressiveness and recurrence.

This paper presents a computational strategy to optimize spectral library size for DIA-MS data, improving peptide and protein identification rates in colorectal and carcinoma samples.

The paper introduces DPHL, a pan-human protein mass spectrometry library built using DIA and PRM, and demonstrates its utility in identifying differentially expressed proteins in prostate cancer and DLBCL.

This paper develops a deep learning method using DIA tensor for phenotype prediction from mass spectrometry data, achieving high accuracy in cancer classification.

This study rapidly generates proteomic data for NCI-60 cancer cell lines and identifies drug response determinants by integrating proteomic, genetic, and transcriptomic data.

This study uses PCT-SWATH to analyze proteomic changes in HCC biopsy samples, identifying 23 upregulated pathways and 13 downregulated ones, with notable changes in virus-infection-related proteins in HBV+ patients.

This study quantifies spatial proteome heterogeneity in prostate tissues using multi-region analysis and identifies biomarker variability across benign and malignant tissues.