BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

162 related articles for article (PubMed ID: 33892256)

  • 1. Predicting Kováts Retention Indices Using Graph Neural Networks.
    Qu C; Schneider BI; Kearsley AJ; Keyrouz W; Allison TC
    J Chromatogr A; 2021 Jun; 1646():462100. PubMed ID: 33892256
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Accurate prediction of isothermal gas chromatographic Kováts retention indices.
    Anjum A; Liigand J; Milford R; Gautam V; Wishart DS
    J Chromatogr A; 2023 Aug; 1705():464176. PubMed ID: 37413909
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.
    Goel P; Bapat S; Vyas R; Tambe A; Tambe SS
    J Chromatogr A; 2015 Nov; 1420():98-109. PubMed ID: 26460075
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A deep convolutional neural network for the estimation of gas chromatographic retention indices.
    Matyushin DD; Sholokhova AY; Buryak AK
    J Chromatogr A; 2019 Dec; 1607():460395. PubMed ID: 31405570
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Simultaneous modeling of the Kovats retention indices on OV-1 and SE-54 stationary phases using artificial neural networks.
    Fatemi MH
    J Chromatogr A; 2002 May; 955(2):273-80. PubMed ID: 12075931
    [TBL] [Abstract][Full Text] [Related]  

  • 6. AIRI: Predicting Retention Indices and Their Uncertainties Using Artificial Intelligence.
    Geer LY; Stein SE; Mallard WG; Slotta DJ
    J Chem Inf Model; 2024 Feb; 64(3):690-696. PubMed ID: 38230885
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Graph convolutional neural network applied to the prediction of normal boiling point.
    Qu C; Kearsley AJ; Schneider BI; Keyrouz W; Allison TC
    J Mol Graph Model; 2022 May; 112():108149. PubMed ID: 35149486
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases.
    Matyushin DD; Sholokhova AY; Buryak AK
    Int J Mol Sci; 2021 Aug; 22(17):. PubMed ID: 34502099
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Cross-column prediction of gas-chromatographic retention indices of saturated esters.
    D'Archivio AA; Maggi MA; Ruggieri F
    J Chromatogr A; 2014 Aug; 1355():269-77. PubMed ID: 24939086
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Deep learning for retention time prediction in reversed-phase liquid chromatography.
    Fedorova ES; Matyushin DD; Plyushchenko IV; Stavrianidi AN; Buryak AK
    J Chromatogr A; 2022 Feb; 1664():462792. PubMed ID: 34999303
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A retention index library for commonly encountered drugs and metabolites using tri-n-alkylamines as reference compounds, nitrogen-phosphorus detectors, and dual capillary chromatography.
    Watts VW; Simonick TF
    J Anal Toxicol; 1987; 11(5):210-4. PubMed ID: 3682780
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Estimation of Kováts retention indices using group contributions.
    Stein SE; Babushok VI; Brown RL; Linstrom PJ
    J Chem Inf Model; 2007; 47(3):975-80. PubMed ID: 17367127
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Steroid identification via deep learning retention time predictions and two-dimensional gas chromatography-high resolution mass spectrometry.
    Randazzo GM; Bileck A; Danani A; Vogt B; Groessl M
    J Chromatogr A; 2020 Feb; 1612():460661. PubMed ID: 31708215
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Quantitative structure-retention relationship for the Kovats retention indices of a large set of terpenes: a combined data splitting-feature selection strategy.
    Hemmateenejad B; Javadnia K; Elyasi M
    Anal Chim Acta; 2007 May; 592(1):72-81. PubMed ID: 17499073
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Development of a database of gas chromatographic retention properties of organic compounds.
    Babushok VI; Linstrom PJ; Reed JJ; Zenkevich IG; Brown RL; Mallard WG; Stein SE
    J Chromatogr A; 2007 Jul; 1157(1-2):414-21. PubMed ID: 17543315
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Wrong gas/liquid partition data by gas chromatography.
    Kováts ES; Kresz R
    J Chromatogr A; 2006 Apr; 1113(1-2):206-19. PubMed ID: 16497316
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Dual graph convolutional neural network for predicting chemical networks.
    Harada S; Akita H; Tsubaki M; Baba Y; Takigawa I; Yamanishi Y; Kashima H
    BMC Bioinformatics; 2020 Apr; 21(Suppl 3):94. PubMed ID: 32321421
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Characterization of 9 gas chromatography columns by Kovats and Lee retention indices for dioxin analysis.
    Stultz C; Dorman F
    J Chromatogr A; 2020 Mar; 1614():460701. PubMed ID: 31866133
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A general procedure for finding potentially erroneous entries in the database of retention indices.
    Khrisanfov MD; Matyushin DD; Samokhin AS
    Anal Chim Acta; 2024 Apr; 1297():342375. PubMed ID: 38438243
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Three-dimensional topographic index applied to the prediction of acyclic C5-C8 alkenes Kováts retention indices on polydimethylsiloxane and squalane columns.
    Ren Y; Liu H; Yao X; Liu M
    J Chromatogr A; 2007 Jun; 1155(1):105-11. PubMed ID: 17466321
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.