Shengbo Wang

The paper presents a high-quality acetylation dataset and shows that transfer learning can achieve good performance with minimal data for identifying PTMs.

The paper creates a high-confidence human methylation atlas and develops a deep learning model for methylated peptide detection.

The paper identifies 25% of 7,264 non-canonical open reading frames (ncORFs) as producing translated gene products, supported by over 3,000 peptides from 3.8 billion mass spectra.

The paper maps the functional importance of site-specific ubiquitination across the human proteome by analyzing conservation, perturbation, and machine learning scores.

The paper reanalyzes public pig proteomics data to map baseline protein expression across 14 organs and compares it with human orthologs.

The paper reanalyzes 24 public proteomics datasets to assess baseline protein abundance in 31 human organs and integrates the results into the Expression Atlas for combined proteomics and transcriptomics analysis.

The paper reanalyzes proteomics datasets from mouse and rat tissues to compare baseline protein expression across species and organs, finding high correlation among orthologs in brain, kidney, heart, and liver.