ArticleBiomolecules2021
A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry.
Article in Biomolecules, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Deep Learning-Enhanced Nanozyme-Based Biosensors for Next-Generation Medical Diagnostics.Biosensors · 2025Review
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- Uncertainty Quantification and Flagging of Unreliable Predictions in Predicting Mass Spectrometry-Related Properties of Small Molecules Using Machine Learning.International journal of molecular sciences · 2024Article
- Deep learning enabled label-free microfluidic droplet classification for single cell functional assays.Frontiers in bioengineering and biotechnology · 2024Article
- Ionmob: a Python package for prediction of peptide collisional cross-section values.Bioinformatics (Oxford, England) · 2023Article
- Collision cross section measurement and prediction methods in omics.Journal of mass spectrometry : JMS · 2023Review
- Intelligent Workflow and Software for Non-Target Analysis of Complex Samples Using a Mixture of Toxic Transformation Products of Unsymmetrical Dimethylhydrazine as an Example.Molecules (Basel, Switzerland) · 2023Article
- Liquid-phase separations coupled with ion mobility-mass spectrometry for next-generation biopharmaceutical analysis.Expert review of proteomicsReview
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most frequently, the identification of peptides in mass spectrometry-based proteomics is carried out using high-resolution tandem mass spectrometry. In order to increase the accuracy of analysis, additional information on the peptides such as chromatographic retention time and collision cross section in ion mobility spectrometry can be used. An accurate prediction of the collision cross section values allows erroneous candidates to be rejected using a comparison of the observed values and the predictions based on the amino acids sequence. Recently, a massive high-quality data set of peptide collision cross sections was released. This opens up an opportunity to apply the most sophisticated deep learning techniques for this task. Previously, it was shown that a recurrent neural network allows for predicting these values accurately. In this work, we present a deep convolutional neural network that enables us to predict these values more accurately compared with previous studies. We use a neural network with complex architecture that contains both convolutional and fully connected layers and comprehensive methods of converting a peptide to multi-channel 1D spatial data and vector. The source code and pre-trained model are available online.
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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.