125 related articles for article (PubMed ID: 38230885)
1. 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]
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. 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]
4. Prediction of HPLC retention index using artificial neural networks and IGroup E-state indices.
Albaugh DR; Hall LM; Hill DW; Kertesz TM; Parham M; Hall LH; Grant DF
J Chem Inf Model; 2009 Apr; 49(4):788-99. PubMed ID: 19309176
[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. GCMS-ID: a webserver for identifying compounds from gas chromatography mass spectrometry experiments.
Wakoli J; Anjum A; Sajed T; Oler E; Wang F; Gautam V; LeVatte M; Wishart DS
Nucleic Acids Res; 2024 Jul; 52(W1):W381-W389. PubMed ID: 38783107
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Use and abuse of retention indices in gas chromatography.
Bizzo HR; Brilhante NS; Nolvachai Y; Marriott PJ
J Chromatogr A; 2023 Oct; 1708():464376. PubMed ID: 37717451
[TBL] [Abstract][Full Text] [Related]
9. Retention index thresholds for compound matching in GC-MS metabolite profiling.
Strehmel N; Hummel J; Erban A; Strassburg K; Kopka J
J Chromatogr B Analyt Technol Biomed Life Sci; 2008 Aug; 871(2):182-90. PubMed ID: 18501684
[TBL] [Abstract][Full Text] [Related]
10. A deep learning-based approach for statistical robustness evaluation in proton therapy treatment planning: a feasibility study.
Vazquez I; Gronberg MP; Zhang X; Court LE; Zhu XR; Frank SJ; Yang M
Phys Med Biol; 2023 Apr; 68(9):. PubMed ID: 37040785
[No Abstract] [Full Text] [Related]
11. [Research progress and application of retention time prediction method based on deep learning].
DU Z; Shao W; Qin W
Se Pu; 2021 Mar; 39(3):211-218. PubMed ID: 34227303
[TBL] [Abstract][Full Text] [Related]
12. Transfer of gas chromatographic retention data among poly(siloxane) columns by quantitative structure-retention relationships based on molecular descriptors of both solutes and stationary phases.
Biancolillo A; D'Archivio AA
J Chromatogr A; 2022 Jan; 1663():462758. PubMed ID: 34954535
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Uncertainty quantification and integration of machine learning techniques for predicting acid rock drainage chemistry: a probability bounds approach.
Betrie GD; Sadiq R; Morin KA; Tesfamariam S
Sci Total Environ; 2014 Aug; 490():182-90. PubMed ID: 24852616
[TBL] [Abstract][Full Text] [Related]
15. Microsatellite Uncertainty Control Using Deterministic Artificial Intelligence.
Wilt E; Sands T
Sensors (Basel); 2022 Nov; 22(22):. PubMed ID: 36433317
[TBL] [Abstract][Full Text] [Related]
16. Reducing uncertainties in neural network Jacobians and improving accuracy of neural network emulations with NN ensemble approaches.
Krasnopolsky VM
Neural Netw; 2007 May; 20(4):454-61. PubMed ID: 17521879
[TBL] [Abstract][Full Text] [Related]
17. Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations.
Arhami M; Kamali N; Rajabi MM
Environ Sci Pollut Res Int; 2013 Jul; 20(7):4777-89. PubMed ID: 23292230
[TBL] [Abstract][Full Text] [Related]
18. An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model.
Moghadam SV; Sharafati A; Feizi H; Marjaie SMS; Asadollah SBHS; Motta D
Environ Monit Assess; 2021 Nov; 193(12):798. PubMed ID: 34773156
[TBL] [Abstract][Full Text] [Related]
19. A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level.
Kearney VP; Yansane AM; Brandon RG; Vaderhobli R; Lin GH; Hekmatian H; Deng W; Joshi N; Bhandari H; Sadat AS; White JM
J Dent; 2022 Aug; 123():104211. PubMed ID: 35760207
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
