ArticleNature communications2025
Deep learning network for NMR spectra reconstruction in time-frequency domain and quality assessment.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
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Who cites it
11 citing papers in PubMed.
- The perspective of artificial intelligence for NMR.Magnetic resonance letters · 2027Review
- High-quality pure shift NMR spectra by deep learning using multi-spectral input and joint loss functions.Nature communications · 2026Article
- Deep Learning Assisted Proton Pure Shift NMR Spectroscopy.Journal of the American Chemical Society · 2026Article
- Fast acquisition of high resolution liquid NMR spectroscopy.Magnetic resonance letters · 2026Review
- Review
- Decoherence Principles and Algorithms for One-Dimensional Nonuniform Sampling Schedules for Multidimensional NMR.Analytical chemistry · 2025Article
- NMR in the Age of Modern Biomedical Research and Drug Discovery.Journal of molecular biology · 2025Review
- Advancements and Challenges in Salivary Metabolomics for Early Detection and Monitoring of Systemic Diseases.MedComm · 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
- Deep learning network for NMR spectra reconstruction in time-frequency domain and quality assessment.Nature communications · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
High-quality nuclear magnetic resonance (NMR) spectra can be rapidly acquired by combining non-uniform sampling techniques (NUS) with reconstruction algorithms. However, current deep learning (DL) based reconstruction methods focus only on single-domain reconstruction (time or frequency domain), leading to drawbacks like peak loss and artifact peaks and ultimately failing to achieve optimal performance. Moreover, the lack of fully sampled spectra makes it difficult, even impossible, to determine the quality of reconstructed spectra, presenting challenges in the practical applications of NUS. In this study, a joint time-frequency domain deep learning network, referred to as JTF-Net, is proposed. It effectively combines time domain and frequency domain features, exhibiting better reconstruction performance on protein spectra across various dimensions compared to traditional algorithms and single-domain DL methods. In addition, the reference-free quality assessment metric, denoted as REconstruction QUalIty assuRancE Ratio (REQUIRER), is proposed base on an established quality space in the field of NMR spectral reconstruction. The metric is capable of evaluating the quality of reconstructed NMR spectra without the fully sampled spectra, making it more suitable for practical applications.
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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.