Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches

scientific article published on 01 November 2019

Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches is …
instance of (P31):
scholarly articleQ13442814

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P8978DBLP publication IDjournals/bib/NguyenNM19
P356DOI10.1093/BIB/BBY066
P932PMC publication ID6954430
P698PubMed publication ID30099485

P50authorHiroshi MamitsukaQ42174915
P2093author name stringCanh Hao Nguyen
Dai Hai Nguyen
P2860cites workMatching structures to mass spectra using fragmentation patterns: are the results as good as they look?Q51838340
Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identificationQ57014238
In silico fragmentation for computer assisted identification of metabolite mass spectraQ21284354
Metabolite Identification Using Automated Comparison of High-Resolution Multistage Mass Spectral TreesQ27156570
Computational mass spectrometry for small moleculesQ27703094
Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular NetworkingQ27818844
Identifying the Unknowns by Aligning Fragmentation TreesQ28530233
FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric dataQ33365583
Computing fragmentation trees from tandem mass spectrometry dataQ33779369
Metabolite identification and molecular fingerprint prediction through machine learning.Q34344462
MetFusion: integration of compound identification strategiesQ34620986
Current progress in computational metabolomicsQ34649443
Searching molecular structure databases with tandem mass spectra using CSI:FingerID.Q36179301
Computational strategies for metabolite identification in metabolomicsQ37810161
Fast metabolite identification with Input Output Kernel RegressionQ39679889
Metabolite identification through multiple kernel learning on fragmentation treesQ40750458
HMDB 4.0: the human metabolome database for 2018.Q43212324
Towards de novo identification of metabolites by analyzing tandem mass spectraQ44805412
Finding maximum colorful subtrees in practiceQ45253594
Substructure-based annotation of high-resolution multistage MS(n) spectral treesQ48046275
P275copyright licenseCreative Commons Attribution-NonCommercial 4.0 InternationalQ34179348
P6216copyright statuscopyrightedQ50423863
P433issue6
P921main subjectmachine learningQ2539
metabolite identificationQ116678277
P304page(s)2028-2043
P577publication date2019-11-01
P1433published inBriefings in BioinformaticsQ4967031
P1476titleRecent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches
P478volume20

Reverse relations

cites work (P2860)
Q90064785ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra
Q62020441Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography–High-Resolution Tandem Mass Spectrometry and NMR Profiling, in Silico Databases, and Chemometrics
Q114868297Computational Metabolomics Tools Reveal Metabolic Reconfigurations Underlying the Effects of Biostimulant Seaweed Extracts on Maize Plants under Drought Stress Conditions
Q64070751Evaluation of Factors Affecting Antimicrobial Activity of Bacteriocin from Microencapsulated in Alginate-Gelatin Capsules and Its Application on Pork Meat as a Bio-Preservative
Q92152476From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data
Q90664075Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features
Q81825158Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models
Q92689119MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra
Q64112118Predicting Ion Mobility Collision Cross-Sections Using a Deep Neural Network: DeepCCS
Q103006029Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification
Q63352284mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics

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