ReviewProteomics2020
Deep Learning in Proteomics.
Review in Proteomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
77 citing papers in PubMed, 2 syntheses or guidelines pooled it, 157 citations in OpenAlex.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- Distinct Functional Signatures of Human Olfactory and Respiratory Mucus Revealed by Proteomics Combined With Machine Learning.Proteomics. Clinical applications · 2026Article
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery.Nature biotechnology · 2026Article
- Review
- Better Inputs, Better Learning: A Peptide Embedding Tutorial for Proteomic Mass Spectrometry.Journal of proteome research · 2026Article
- Assessing the Performance of Mass Spectrometry Search Strategies in Identifying Translational Errors Using PDX Proteomics Data.Molecular & cellular proteomics : MCP · 2026Article
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- The State of Peptide Detectability in Computational Proteomics and Guidelines for AI Applications.Computational and structural biotechnology journal · 2026Article
- Proteome-Based Biomarkers for Alzheimer's Disease: Old Acquisitions and Innovative Proposals.International journal of molecular sciences · 2025Review
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- Towards Spectral Variation Analysis: A Data Quality Framework for Non-Targeted Methods.Molecules (Basel, Switzerland) · 2025Article
- Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.Nature communications · 2025Article
- A deep learning framework for lysine 2-hydroxyisobutyrylation site prediction using evolutionary feature representation.Scientific reports · 2025Article
- Rank-based learning: a novel high-throughput algorithm resilient to missing data and effective for datasets with small sample size.Briefings in bioinformatics · 2025Article
- Artificial Intelligence and the Evolving Landscape of Immunopeptidomics.Proteomics. Clinical applications · 2025Review
- Gradient responsive regularization: a deep learning framework for codon frequency based classification of evolutionarily conserved genes.BMC genomic data · 2025Article
- DeepSecMS Advances DIA-Based Selenoproteome Profiling Through Cys-to-Sec Proxy Training.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Deep-Learning Driven Identification of Novel Antimicrobial Peptides.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- Carafe enables high qualitybioRxiv : the preprint server for biology · 2025Article
17 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
7 authors at 2 institutions in 2 countries.
Funding
Abstract
Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.
Indexed as
Identifiers
What OpenQuestion holds
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.