ReviewCell reports methods2021
Deep learning neural network tools for proteomics.
Review in Cell reports methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis 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
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Host cell protein impurities in therapeutic proteins: overview of advances in detection, nonconventional removal technologies and immunogenicity assessment.Journal of biological engineering · 2026Review
- A Framework for Database Search with AI Models in Mass Spectrometry-Based Proteomics.Journal of proteome research · 2026Review
- Review
- Review
- End-to-end deep attention-based multitask pipeline for predicting uncertainty-quantified peptide properties from mass spectrometry data.Scientific reports · 2026Article
- Multi-modal deep learning model for bipolar depression adolescents with verbal auditory hallucinations.Frontiers in psychiatry · 2026Article
- Prediction of influenza virus infection based on deep learning and peripheral blood proteomics: A diagnostic study.Journal of advanced research · 2026Article
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.Journal of mass spectrometry : JMS · 2025Article
- Innovative data augmentation strategy for deep learning on biological datasets with limited gene representations focused on chloroplast genomes.Scientific reports · 2025Article
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- A Fast Neural Network for Isotopic Charge State Assignment.Journal of the American Chemical Society · 2025Article
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- Bioanalysis of antihypertensive drugs by LC-MS: a fleeting look at the regulatory guidelines and artificial intelligence.Bioanalysis · 2025Review
- Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics.Analytical chemistry · 2025Review
- A Scalable, Web-Based Platform for Proteomics Data Processing, Result Storage and Analysis.Journal of proteome research · 2025Article
- The analysis of rural revitalization serviceplatform in smart city under back propagation neural network.PloS one · 2025Article
- Article
- Advancing plant biology through deep learning-powered natural language processing.Plant cell reports · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mass-spectrometry-based proteomics enables quantitative analysis of thousands of human proteins. However, experimental and computational challenges restrict progress in the field. This review summarizes the recent flurry of machine-learning strategies using artificial deep neural networks (or "deep learning") that have started to break barriers and accelerate progress in the field of shotgun proteomics. Deep learning now accurately predicts physicochemical properties of peptides from their sequence, including tandem mass spectra and retention time. Furthermore, deep learning methods exist for nearly every aspect of the modern proteomics workflow, enabling improved feature selection, peptide identification, and protein inference.
Indexed as
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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.