ArticleScience advances2018
Deep neural network processing of DEER data.
Article in Science advances, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers.
What it found
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Who cites it
114 citing papers in PubMed.
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- Two-dimensional NMR from a single pulse: Reconstructing heteronuclear 2D spectra via off-resonance decoupling and deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- Versatile High-Sensitivity EPR Using Superconducting Spiral Microresonators.Small methods · 2026Article
- Electron Paramagnetic Resonance Spectroscopy Reveals Promoter Dependent Transcription Regulation by Copper Activated CueR in Pseudomonas aeruginosa.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- A biased allosteric modulator functions as a molecular glue to induce βResearch square · 2025Article
- A biased allosteric modulator functions as a molecular glue to induce βbioRxiv : the preprint server for biology · 2025Article
- Rational Design of a Cu(II) Spin Label Improves the Sensitivity of Distance Measurements.The journal of physical chemistry letters · 2025Article
- EPR spectroscopy in the study of ribosomal complexes.Biophysical reviews · 2025Review
- Scalable deep learning reconstruction for accelerated multidimensional nuclear magnetic resonance spectroscopy of proteins.Science advances · 2025Article
- High-confidence reconstruction for Laplace inversion in NMR based on uncertainty-informed deep learning.Science advances · 2025Article
- Rigid and stable nitroxide spin label for high-resolution distance measurements on proteins by DEER experiments.Magnetic resonance letters · 2025Article
- Q-Band Double Quantum Coherence ESR for Sensitive Nitroxide-Based Distance Measurements.The journal of physical chemistry. B · 2025Article
- Calcium-Dependent S100A8 Amyloid Fibril Formation via S100A1-Mediated Transient Interaction.ACS chemical neuroscience · 2025Article
54 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The established model-free methods for the processing of two-electron dipolar spectroscopy data [DEER (double electron-electron resonance), PELDOR (pulsed electron double resonance), DQ-EPR (double-quantum electron paramagnetic resonance), RIDME (relaxation-induced dipolar modulation enhancement), etc.] use regularized fitting. In this communication, we describe an attempt to process DEER data using artificial neural networks trained on large databases of simulated data. Accuracy and reliability of neural network outputs from real experimental data were found to be unexpectedly high. The networks are also able to reject exchange interactions and to return a measure of uncertainty in the resulting distance distributions. This paper describes the design of the training databases, discusses the training process, and rationalizes the observed performance. Neural networks produced in this work are incorporated as options into Spinach and DeerAnalysis packages.
Identifiers
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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.