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ZENODO
Dataset . 2017
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2017
License: CC BY
Data sources: Datacite
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Data Used In "Fast Metabolite Identification With Input Output Kernel Regression"

Authors: Brouard, Céline; Shen, Huibin; Dührkop, Kai; d'Alché-Buc, Florence; Böcker, Sebastian; Rousu, Juho;

Data Used In "Fast Metabolite Identification With Input Output Kernel Regression"

Abstract

This repository contains the data used in [1] to evaluate the performance for metabolite identification from tandem mass spectra. These data have been extracted and processed in [2]. We used a subset of 4138 MS/MS spectra extracted from the GNPS public spectral library (https://gnps.ucsd.edu/ProteoSAFe/libraries.jsp) for training and evaluation. For searching, we used molecular structures from PubChem as candidate sets. Please mention and cite GNPS when using these data. The implementation of the method proposed in [1] is available on: https://version.aalto.fi/gitlab/kepaco/Fast-metabolite-identification-with-IOKR Files description: spectra.txt: informations about the MS/MS spectra (GNPS identifier, compound name and INCHI identifier) data_GNPS.mat: contains the molecular fingerprints, molecular formula and InCHI corresponding to the MS/MS spectra cv_ind.txt: indices of the cross-validation folds ind_eval.txt: indices of the examples used for evaluation candidates: fingerprints and INCHI for the different candidate sets input_kernels: contains 24 input kernel matrices References: [1] Brouard, C., Shen, H., Dührkop, K., d'Alché-Buc, F., Böcker, S. and Rousu, J.: Fast metabolite identification with Input Output Kernel Regression. In the proceedings of ISMB 2016, Bioinformatics 32(12): i28-i36, 2016. DOI: https://doi.org/10.1093/bioinformatics/btw246 [2] Dührkop, K., Shen, H., Meusel, M., Rousu, J. and Böcker, S.: Searching molecular structure databases with tandem mass spectra using CSI:FingerID. PNAS, 112(41), 12580-12585, 2015. doi:10.1073/pnas.1509788112

Keywords

FOS: Computer and information sciences, Mass spectrometry, Structured prediction, Bioinformatics, Machine learning, Metabolomics, Metabolite identification

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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