ArticleGenomics, proteomics & bioinformatics2014
Bayesian peak picking for NMR spectra.
Article in Genomics, proteomics & bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed.
- UnidecNMR: automatic peak detection for NMR spectra in 1-4 dimensions.Nature communications · 2025Article
- Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose-Tissue-Derived Human Mesenchymal Stem Cells EmployingMetabolites · 2023Article
- Fundamental and practical aspects of machine learning for the peak picking of biomolecular NMR spectra.Journal of biomolecular NMR · 2022Article
- Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches.Natural product reports · 2021Review
- DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra.Nature communications · 2021Article
- iPick: Multiprocessing software for integrated NMR signal detection and validation.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2021Article
- A fast fiducial marker tracking model for fully automatic alignment in electron tomography.Bioinformatics (Oxford, England) · 2018Article
- INFOS: spectrum fitting software for NMR analysis.Journal of biomolecular NMR · 2017Article
- Automation of peak-tracking analysis of stepwise perturbed NMR spectra.Journal of biomolecular NMR · 2017Article
- NMRFAM-SDF: a protein structure determination framework.Journal of biomolecular NMR · 2015Article
- Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra.Scientific reports · 2015Article
- An automated framework for NMR resonance assignment through simultaneous slice picking and spin system forming.Journal of biomolecular NMR · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein structure determination is a very important topic in structural genomics, which helps people to understand varieties of biological functions such as protein-protein interactions, protein-DNA interactions and so on. Nowadays, nuclear magnetic resonance (NMR) has often been used to determine the three-dimensional structures of protein in vivo. This study aims to automate the peak picking step, the most important and tricky step in NMR structure determination. We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem. Under the Bayesian framework, the peak picking problem is casted as a variable selection problem. The proposed method can automatically distinguish true peaks from false ones without preprocessing the data. To the best of our knowledge, this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method.
Indexed as
Identifiers
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.