194 related articles for article (PubMed ID: 22563067)
1. Two effective methods for correcting experimental high-throughput screening data.
Dragiev P; Nadon R; Makarenkov V
Bioinformatics; 2012 Jul; 28(13):1775-82. PubMed ID: 22563067
[TBL] [Abstract][Full Text] [Related]
2. Systematic error detection in experimental high-throughput screening.
Dragiev P; Nadon R; Makarenkov V
BMC Bioinformatics; 2011 Jan; 12():25. PubMed ID: 21247425
[TBL] [Abstract][Full Text] [Related]
3. Detecting and removing multiplicative spatial bias in high-throughput screening technologies.
Caraus I; Mazoure B; Nadon R; Makarenkov V
Bioinformatics; 2017 Oct; 33(20):3258-3267. PubMed ID: 28633418
[TBL] [Abstract][Full Text] [Related]
4. An Automatic Quality Control Pipeline for High-Throughput Screening Hit Identification.
Zhai Y; Chen K; Zhong Y; Zhou B; Ainscow E; Wu YT; Zhou Y
J Biomol Screen; 2016 Sep; 21(8):832-41. PubMed ID: 27313114
[TBL] [Abstract][Full Text] [Related]
5. Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions.
Caraus I; Alsuwailem AA; Nadon R; Makarenkov V
Brief Bioinform; 2015 Nov; 16(6):974-86. PubMed ID: 25750417
[TBL] [Abstract][Full Text] [Related]
6. Novel trends in high-throughput screening.
Mayr LM; Bojanic D
Curr Opin Pharmacol; 2009 Oct; 9(5):580-8. PubMed ID: 19775937
[TBL] [Abstract][Full Text] [Related]
7. HTS-Corrector: software for the statistical analysis and correction of experimental high-throughput screening data.
Makarenkov V; Kevorkov D; Zentilli P; Gagarin A; Malo N; Nadon R
Bioinformatics; 2006 Jun; 22(11):1408-9. PubMed ID: 16595559
[TBL] [Abstract][Full Text] [Related]
8. Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening.
Mazoure B; Caraus I; Nadon R; Makarenkov V
SLAS Discov; 2018 Jun; 23(5):448-458. PubMed ID: 29346010
[TBL] [Abstract][Full Text] [Related]
9. An efficient method for the detection and elimination of systematic error in high-throughput screening.
Makarenkov V; Zentilli P; Kevorkov D; Gagarin A; Malo N; Nadon R
Bioinformatics; 2007 Jul; 23(13):1648-57. PubMed ID: 17463024
[TBL] [Abstract][Full Text] [Related]
10. Plate-based diversity subset screening generation 2: an improved paradigm for high-throughput screening of large compound files.
Bell AS; Bradley J; Everett JR; Loesel J; McLoughlin D; Mills J; Peakman MC; Sharp RE; Williams C; Zhu H
Mol Divers; 2016 Nov; 20(4):789-803. PubMed ID: 27631533
[TBL] [Abstract][Full Text] [Related]
11. Statistical analysis of systematic errors in high-throughput screening.
Kevorkov D; Makarenkov V
J Biomol Screen; 2005 Sep; 10(6):557-67. PubMed ID: 16103415
[TBL] [Abstract][Full Text] [Related]
12. A rapid and affordable screening platform for membrane protein trafficking.
Snyder JC; Pack TF; Rochelle LK; Chakraborty SK; Zhang M; Eaton AW; Bai Y; Ernst LA; Barak LS; Waggoner AS; Caron MG
BMC Biol; 2015 Dec; 13():107. PubMed ID: 26678094
[TBL] [Abstract][Full Text] [Related]
13. Experimental design and statistical methods for improved hit detection in high-throughput screening.
Malo N; Hanley JA; Carlile G; Liu J; Pelletier J; Thomas D; Nadon R
J Biomol Screen; 2010 Sep; 15(8):990-1000. PubMed ID: 20817887
[TBL] [Abstract][Full Text] [Related]
14. Optimal design for high-throughput screening via false discovery rate control.
Feng T; Basu P; Sun W; Ku HT; Mack WJ
Stat Med; 2019 Jul; 38(15):2816-2827. PubMed ID: 30924183
[TBL] [Abstract][Full Text] [Related]
15. Hypothesis testing in high-throughput screening for drug discovery.
Prummer M
J Biomol Screen; 2012 Apr; 17(4):519-29. PubMed ID: 22233646
[TBL] [Abstract][Full Text] [Related]
16. Rethinking molecular similarity: comparing compounds on the basis of biological activity.
Petrone PM; Simms B; Nigsch F; Lounkine E; Kutchukian P; Cornett A; Deng Z; Davies JW; Jenkins JL; Glick M
ACS Chem Biol; 2012 Aug; 7(8):1399-409. PubMed ID: 22594495
[TBL] [Abstract][Full Text] [Related]
17. Using information from historical high-throughput screens to predict active compounds.
Riniker S; Wang Y; Jenkins JL; Landrum GA
J Chem Inf Model; 2014 Jul; 54(7):1880-91. PubMed ID: 24933016
[TBL] [Abstract][Full Text] [Related]
18. Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features.
Murie C; Barette C; Lafanechère L; Nadon R
J Biomol Screen; 2014 Jun; 19(5):661-71. PubMed ID: 24352083
[TBL] [Abstract][Full Text] [Related]
19. Implementation and Use of State-of-the-Art, Cell-Based In Vitro Assays.
Langer G
Handb Exp Pharmacol; 2016; 232():171-90. PubMed ID: 26424721
[TBL] [Abstract][Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]