ArticleNature communications2020
In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
98 citing papers in PubMed, 242 citations in OpenAlex.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Deep Learning Prediction of O-Glycopeptide Tandem Mass Spectra Enhances O-Glycoproteomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Immunopeptidome profiling in pulmonary fibrosis provides a platform for identifying therapeutic targets.Nature immunology · 2026Article
- Full-DIA enables complete single-cell proteomics from diaPASEF using deep learning.Genome biology · 2026Article
- Post-translational modifications of silk proteins.RSC chemical biology · 2026Review
- Carafe2 enables high qualitybioRxiv : the preprint server for biology · 2026Article
- Advances in Mass Spectrometry Instrumentation and Methodology for Analysis of Alternative Protein Isoforms.Journal of mass spectrometry : JMS · 2026Review
- XL-MSDigger: a deep learning-based, versatile solution for cross-linking mass spectrometry.Nature communications · 2026Article
- Better Inputs, Better Learning: A Peptide Embedding Tutorial for Proteomic Mass Spectrometry.Journal of proteome research · 2026Article
- Comparative performance of Scribe and database search engines in metaproteomic profiling of a ground-truth microbiome dataset.Journal of proteomics · 2026Article
- The State of Peptide Detectability in Computational Proteomics and Guidelines for AI Applications.Computational and structural biotechnology journal · 2026Article
- The evolving landscape of spatial proteomics technologies in the AI age.Fundamental research · 2026Review
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- Benchmarking informatics workflows for data-independent acquisition single-cell proteomics.Nature communications · 2025Article
- Massive deciphering of the tissue nucleic acid-binding proteome via affinity chromatography integrated with data-independent acquisition-based proteomics.Nucleic acids research · 2025Article
- Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.Nature communications · 2025Article
- Artificial Intelligence and the Evolving Landscape of Immunopeptidomics.Proteomics. Clinical applications · 2025Review
- FastSpel: A Method for Fast Spectral Library Generation.Journal of proteome research · 2025Article
- DeepSecMS Advances DIA-Based Selenoproteome Profiling Through Cys-to-Sec Proxy Training.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Backyard Proteomics: A Case Study with the Black Widow Spider.Journal of proteome research · 2025Article
38 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA analysis, which is time-consuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.