334 related articles for article (PubMed ID: 19747683)
1. Quantitative structure-retention relationship studies for taxanes including epimers and isomeric metabolites in ultra fast liquid chromatography.
Dong PP; Ge GB; Zhang YY; Ai CZ; Li GH; Zhu LL; Luan HW; Liu XB; Yang L
J Chromatogr A; 2009 Oct; 1216(42):7055-62. PubMed ID: 19747683
[TBL] [Abstract][Full Text] [Related]
2. Prediction of capillary gas chromatographic retention times of fatty acid methyl esters in human blood using MLR, PLS and back-propagation artificial neural networks.
Gupta VK; Khani H; Ahmadi-Roudi B; Mirakhorli S; Fereyduni E; Agarwal S
Talanta; 2011 Jan; 83(3):1014-22. PubMed ID: 21147352
[TBL] [Abstract][Full Text] [Related]
3. Prediction of retention indices of drugs based on immobilized artificial membrane chromatography using Projection Pursuit Regression and Local Lazy Regression.
Du H; Watzl J; Wang J; Zhang X; Yao X; Hu Z
J Sep Sci; 2008 Jul; 31(12):2325-33. PubMed ID: 18491354
[TBL] [Abstract][Full Text] [Related]
4. Modelling of retention of pesticides in reversed-phase high-performance liquid chromatography: quantitative structure-retention relationships based on solute quantum-chemical descriptors and experimental (solvatochromic and spin-probe) mobile phase descriptors.
D'Archivio AA; Ruggieri F; Mazzeo P; Tettamanti E
Anal Chim Acta; 2007 Jun; 593(2):140-51. PubMed ID: 17543600
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Comparative multiple quantitative structure-retention relationships modeling of gas chromatographic retention time of essential oils using multiple linear regression, principal component regression, and partial least squares techniques.
Qin LT; Liu SS; Liu HL; Tong J
J Chromatogr A; 2009 Jul; 1216(27):5302-12. PubMed ID: 19486989
[TBL] [Abstract][Full Text] [Related]
7. Predicting liquid chromatographic retention times of peptides from the Drosophila melanogaster proteome by machine learning approaches.
Tian F; Yang L; Lv F; Zhou P
Anal Chim Acta; 2009 Jun; 644(1-2):10-6. PubMed ID: 19463555
[TBL] [Abstract][Full Text] [Related]
8. Prediction of gas chromatography/electron capture detector retention times of chlorinated pesticides, herbicides, and organohalides by multivariate chemometrics methods.
Ghasemi J; Asadpour S; Abdolmaleki A
Anal Chim Acta; 2007 Apr; 588(2):200-6. PubMed ID: 17386811
[TBL] [Abstract][Full Text] [Related]
9. Quantitative study of the structure-retention index relationship in the imine family.
Acevedo-Martínez J; Escalona-Arranz JC; Villar-Rojas A; Téllez-Palmero F; Pérez-Rosés R; González L; Carrasco-Velar R
J Chromatogr A; 2006 Jan; 1102(1-2):238-44. PubMed ID: 16288769
[TBL] [Abstract][Full Text] [Related]
10. Artificial neural network prediction of retention factors of some benzene derivatives and heterocyclic compounds in micellar electrokinetic chromatography.
Golmohammadi H; Fatemi MH
Electrophoresis; 2005 Sep; 26(18):3438-44. PubMed ID: 16110463
[TBL] [Abstract][Full Text] [Related]
11. Review on modelling aspects in reversed-phase liquid chromatographic quantitative structure-retention relationships.
Put R; Vander Heyden Y
Anal Chim Acta; 2007 Oct; 602(2):164-72. PubMed ID: 17933600
[TBL] [Abstract][Full Text] [Related]
12. Combination of artificial neural network technique and linear free energy relationship parameters in the prediction of gradient retention times in liquid chromatography.
Fatemi MH; Abraham MH; Poole CF
J Chromatogr A; 2008 May; 1190(1-2):241-52. PubMed ID: 18395736
[TBL] [Abstract][Full Text] [Related]
13. Comprehensive comparison of eight statistical modelling methods used in quantitative structure-retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome.
Zhou P; Tian F; Lv F; Shang Z
J Chromatogr A; 2009 Apr; 1216(15):3107-16. PubMed ID: 19232620
[TBL] [Abstract][Full Text] [Related]
14. Rapid qualitative and quantitative determination of seven valuable taxanes from various Taxus species by UFLC-ESI-MS and UFLC-DAD.
Ge GB; Liang SC; Hu Y; Liu XB; Mao YX; Zhang YY; Luan HW; Qiu MH; Yang L
Planta Med; 2010 Oct; 76(15):1773-7. PubMed ID: 20506080
[TBL] [Abstract][Full Text] [Related]
15. Investigation of retention behaviour of non-steroidal anti-inflammatory drugs in high-performance liquid chromatography by using quantitative structure-retention relationships.
Carlucci G; D'Archivio AA; Maggi MA; Mazzeo P; Ruggieri F
Anal Chim Acta; 2007 Oct; 601(1):68-76. PubMed ID: 17904471
[TBL] [Abstract][Full Text] [Related]
16. Cross-column prediction of gas-chromatographic retention of polychlorinated biphenyls by artificial neural networks.
D'Archivio AA; Incani A; Ruggieri F
J Chromatogr A; 2011 Dec; 1218(48):8679-90. PubMed ID: 22000780
[TBL] [Abstract][Full Text] [Related]
17. Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives.
Mandal AS; Roy K
Eur J Med Chem; 2009 Apr; 44(4):1509-24. PubMed ID: 18760864
[TBL] [Abstract][Full Text] [Related]
18. Quantitative structure-retention relationships of pesticides in reversed-phase high-performance liquid chromatography.
Aschi M; D'Archivio AA; Maggi MA; Mazzeo P; Ruggieri F
Anal Chim Acta; 2007 Jan; 582(2):235-42. PubMed ID: 17386498
[TBL] [Abstract][Full Text] [Related]
19. Prediction of gas chromatographic retention indices of some amino acids and carboxylic acids from their structural descriptors.
Fatemi MH; Elyasi M
J Sep Sci; 2011 Nov; 34(22):3216-20. PubMed ID: 22012944
[TBL] [Abstract][Full Text] [Related]
20. Advanced QSRR modeling of peptides behavior in RPLC.
Bodzioch K; Durand A; Kaliszan R; Baczek T; Vander Heyden Y
Talanta; 2010 Jun; 81(4-5):1711-8. PubMed ID: 20441962
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
