Contact and support
Need help, have a question, or want to contact the ResearchHub team?
© 2026 ResearchHub. Built for responsible scholarly connection.
Privacy-aware community infrastructureResearch graph
References from Deep metabolome: Applications of deep learning in metabolomics. Local targets link to admitted publications; unresolved targets remain external evidence.
Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine
10.1089/omi.2018.0097 · 2018 · External reference
Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration
2016 · External reference
LC-MS alignment in theory and practice: a comprehensive algorithmic review
10.1093/bib/bbt080 · 2013 · External reference
10.3389/fbioe.2015.00023
10.3389/fbioe.2015.00023 · External reference
Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches
10.1093/bib/bby066 · 2018 · External reference
Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers
10.1093/bib/bbv077 · 2015 · External reference
10.1016/s0167-7012(00)00201-3
10.1016/s0167-7012(00)00201-3 · External reference
Unresolved reference
External reference
10.1016/j.cels.2016.01.009
10.1016/j.cels.2016.01.009 · External reference
Unresolved reference
External reference
Unresolved reference
2019 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
10.1007/978-1-4842-2845-6_1
10.1007/978-1-4842-2845-6_1 · External reference
Unresolved reference
External reference
Unresolved reference
2018 · External reference
10.1007/978-1-4842-3679-6
10.1007/978-1-4842-3679-6 · External reference
Survey and critique of techniques for extracting rules from trained artificial neural networks
10.1016/0950-7051(96)81920-4 · 1995 · External reference
Artificial neural networks: a tutorial
10.1109/2.485891 · 1996 · External reference
10.1561/2200000006 · Admitted local publication
Computational Methods for the Discovery of Metabolic Markers of Complex Traits
2019 · External reference
10.1007/s11306-019-1608-0
10.1007/s11306-019-1608-0 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Centrum voor Wiskunde en Informatica Amsterdam
1995 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Unresolved reference
External reference
Unresolved reference
External reference
Unresolved reference
2020 · External reference
Deep learning using rectified linear units (relu)
2018 · External reference
Unresolved reference
External reference
10.1038/nrd.2016.32
10.1038/nrd.2016.32 · External reference
Unresolved reference
2019 · External reference
Processing and Analysis of Untargeted Multicohort NMR Data
10.1007/978-1-4939-9690-2_25 · 2019 · External reference
Analysis of NMR Metabolomics Data
10.1007/978-1-0716-0239-3_5 · 2020 · External reference
Non-Uniform Sampling for All: More NMR Spectral Quality, Less Measurement Time
2017 · External reference
Two dimensional NMR spectroscopic approaches for exploring plant metabolome: A review
10.1016/j.jare.2014.10.003 · 2015 · External reference
NMR signal processing, prediction, and structure verification with machine learning techniques
2020 · External reference
10.1007/s10858-019-00265-1
10.1007/s10858-019-00265-1 · External reference
Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
2019 · External reference
Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine
10.3389/fphar.2017.00474 · 2017 · External reference
10.1016/j.csbj.2019.04.009 · Admitted local publication
Data processing for mass spectrometry-based metabolomics
10.1016/j.chroma.2007.04.021 · 2007 · External reference
10.1021/ac051437y
10.1021/ac051437y · External reference
Knowledge discovery in metabolomics: An overview of MS data handling
10.1002/jssc.200900609 · 2010 · External reference
MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data
10.1186/1471-2105-11-395 · 2010 · External reference
metaMS: An open-source pipeline for GC–MS-based untargeted metabolomics
10.1016/j.jchromb.2014.02.051 · 2014 · External reference
MetAlign 3.0: performance enhancement by efficient use of advances in computer hardware
10.1007/s11306-011-0369-1 · 2012 · External reference
Gas chromatography – mass spectrometry data processing made easy
10.1016/j.chroma.2017.04.052 · 2017 · External reference
Deep Learning for the Precise Peak Detection in High-Resolution LC–MS Data
10.1021/acs.analchem.9b04811 · 2020 · External reference
Comparative evaluation of software for retention time alignment of gas chromatography/time-of-flight mass spectrometry-based metabonomic data
10.1016/j.chroma.2010.10.101 · 2010 · External reference
Using deep learning to evaluate peaks in chromatographic data
10.1016/j.talanta.2019.05.053 · 2019 · External reference
PARAFAC2—Part II. Modeling chromatographic data with retention time shifts
10.1002/(sici)1099-128x(199905/08)13:3/4<295::aid-cem547>3.0.co;2-y · 1999 · External reference
Peak alignment of gas chromatography–mass spectrometry data with deep learning
10.1016/j.chroma.2019.460476 · 2019 · External reference
Deep Neural Networks for Classification of LC-MS Spectral Peaks
10.1021/acs.analchem.9b02983 · 2019 · External reference
Large-Scale Prediction of Collision Cross-Section Values for Metabolites in Ion Mobility-Mass Spectrometry
10.1021/acs.analchem.6b03091 · 2016 · External reference
Development of a Reverse Phase HPLC Retention Index Model for Nontargeted Metabolomics Using Synthetic Compounds
10.1021/acs.jcim.7b00496 · 2018 · External reference
Deep MS/MS-Aided Structural-Similarity Scoring for Unknown Metabolite Identification
10.1021/acs.analchem.8b05405 · 2019 · External reference
10.1093/nar/gkx1089
10.1093/nar/gkx1089 · External reference
10.1021/acs.analchem.7b04424
10.1021/acs.analchem.7b04424 · External reference
10.1002/jms.1777
10.1002/jms.1777 · External reference
Comparative analysis of mass spectral similarity measures on peak alignment for comprehensive two-dimensional gas chromatography mass spectrometry
2013 · External reference
10.1016/1044-0305(94)87009-8
10.1016/1044-0305(94)87009-8 · External reference
Unresolved reference
External reference
Deep learning-based component identification for the Raman spectra of mixtures
10.1039/c8an02212g · 2019 · External reference
Predicting Ion Mobility Collision Cross-Sections Using a Deep Neural Network: DeepCCS
10.1021/acs.analchem.8b05821 · 2019 · External reference
Unresolved reference
2018 · External reference
Spectral deep learning for prediction and prospective validation of functional groups
2020 · External reference
10.3390/metabo8020031
10.3390/metabo8020031 · External reference
RxnSim: a tool to compare biochemical reactions
10.1093/bioinformatics/btv416 · 2015 · External reference
Computational Prediction of Electron Ionization Mass Spectra to Assist in GC/MS Compound Identification
10.1021/acs.analchem.6b01622 · 2016 · External reference
Rapid Prediction of Electron-Ionization Mass Spectrometry Using Neural Networks
10.1021/acscentsci.9b00085 · 2019 · External reference
10.1021/ci100050t
10.1021/ci100050t · External reference
LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision To Support Ion Mobility-Mass Spectrometry-Based Lipidomics
10.1021/acs.analchem.7b02625 · 2017 · External reference
10.1021/acs.analchem.9b02348
10.1021/acs.analchem.9b02348 · External reference
Collision cross section compendium to annotate and predict multi-omic compound identities
10.1039/c8sc04396e · 2019 · External reference
Data preprocessing in predictive data mining
10.1017/s026988891800036x · 2019 · External reference
A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification
10.1007/s11306-019-1612-4 · 2019 · External reference
Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix
10.1002/uog.20168 · 2019 · External reference
Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data
10.1021/acs.jproteome.7b00595 · 2018 · External reference
Deep Learning Based Drug Metabolites Prediction
10.3389/fphar.2019.01586 · 2020 · External reference
SyGMa: Combining Expert Knowledge and Empirical Scoring in the Prediction of Metabolites
10.1002/cmdc.200700312 · 2008 · External reference
Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics
10.3390/metabo7020030 · 2017 · External reference
Application of ensemble deep neural network to metabolomics studies
10.1016/j.aca.2018.02.045 · 2018 · External reference
10.1098/rsif.2017.0387
10.1098/rsif.2017.0387 · External reference
10.1038/s41576-019-0122-6
10.1038/s41576-019-0122-6 · External reference
10.1016/j.ymeth.2019.03.004
10.1016/j.ymeth.2019.03.004 · External reference
A review on machine learning principles for multi-view biological data integration
2018 · External reference
Unresolved reference
External reference
Unresolved reference
2020 · External reference
Unresolved reference
External reference
10.3390/metabo9060108
10.3390/metabo9060108 · External reference
10.3390/metabo9040076
10.3390/metabo9040076 · External reference
Statistical Modeling: The Two Cultures
10.1214/ss/1009213726 · 2001 · External reference
Unresolved reference
External reference
Unresolved reference
External reference
Deep Neural Networks for High Dimension
2017 · External reference
10.1038/nbt.3300
10.1038/nbt.3300 · External reference
10.1038/nmeth.3547
10.1038/nmeth.3547 · External reference
10.1101/gr.200535.115
10.1101/gr.200535.115 · External reference
DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture
10.1038/s41598-019-47765-6 · 2019 · External reference
Sample size planning for classification models
10.1016/j.aca.2012.11.007 · 2013 · External reference
A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods
10.1093/biomet/76.3.503 · 1989 · External reference
10.1186/1471-2105-7-91
10.1186/1471-2105-7-91 · External reference
Machine learning algorithm validation with a limited sample size
10.1371/journal.pone.0224365 · 2019 · External reference
