ArticlePLoS computational biology2017
DeepPep: Deep proteome inference from peptide profiles.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The evolving landscape of spatial proteomics technologies in the AI age.Fundamental research · 2026Review
- HGCPep: Hypergraph Deep Learning Identifies Cancer-associated Non-coding Peptides.Genomics, proteomics & bioinformatics · 2025Article
- Antimicrobial peptide prediction based on contrastive learning and gated convolutional neural network.Scientific reports · 2025Article
- Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps.Cancers · 2023Review
- Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups.Molecular & cellular proteomics : MCP · 2022Article
- MetaLP: An integrative linear programming method for protein inference in metaproteomics.PLoS computational biology · 2022Article
- Artificial intelligence: A powerful paradigm for scientific research.Innovation (Cambridge (Mass.)) · 2021Review
- Deep learning neural network tools for proteomics.Cell reports methods · 2021Review
- Adaption of the Aristotle Classifier for Accurately Identifying Highly Similar Bacteria Analyzed by MALDI-TOF MS.Analytical chemistry · 2020Article
- The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.Frontiers in microbiology · 2020Review
- Quality control of imbalanced mass spectra from isotopic labeling experiments.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Protein inference, the identification of the protein set that is the origin of a given peptide profile, is a fundamental challenge in proteomics. We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile. In its core, DeepPep quantifies the change in probabilistic score of peptide-spectrum matches in the presence or absence of a specific protein, hence selecting as candidate proteins with the largest impact to the peptide profile. Application of the method across datasets argues for its competitive predictive ability (AUC of 0.80±0.18, AUPR of 0.84±0.28) in inferring proteins without need of peptide detectability on which the most competitive methods rely. We find that the convolutional neural network architecture outperforms the traditional artificial neural network architectures without convolution layers in protein inference. We expect that similar deep learning architectures that allow learning nonlinear patterns can be further extended to problems in metagenome profiling and cell type inference. The source code of DeepPep and the benchmark datasets used in this study are available at https://deeppep.github.io/DeepPep/.
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