Using support vector machine regression to model the retention of peptides in immobilized metal-affinity chromatography

Sensors and Actuators, B: Chemical

Volumn 125, Issue 1, 2007, Pages 149-157

  Kermani, B G ^a   Kozlov, I ^a   Melnyk, P ^a   Zhao, C ^a   Hachmann, J ^a   Barker, D ^a   Lebl, M ^a  

^a ILLUMINA INC   (United States)

Author keywords

Metal affinity chromatography; Peptide; Regression; Retention time; Support vector machines

Indexed keywords

AFFINITY CHROMATOGRAPHY; AMINO ACIDS; NICKEL; PEPTIDES; REGRESSION ANALYSIS;

HISTIDINE; IMMOBILIZED METAL-AFFINITY CHROMATOGRAPHY (IMAC); ISOELECTRIC POINT; RETENTION TIME;

SUPPORT VECTOR MACHINES;

EID: 34347366084     PISSN: 09254005     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.snb.2007.02.004     Document Type: Article

References (17)

1. Porath J., Carlsson J., Olsson I., and Belfrange G. Metal chelate affinity chromatography, a new approach to protein fractionation. Nature 258 (1975) 598-599
2. Hochuli E., Dobeli H., and Schacher A. New metal chelate adsorbent selective for proteins and peptides containing neighboring histidine residues. J. Chromatogr. 411 (1987) 177-184
3. Sulkowski E. Purification of proteins by IMAC. Trends Biotechnol. 3 (1985) 1-7
4. Smith M.C., Furman T.C., Ingolia T.D., and Pidgeon C. Chelating peptide-immobilized metal ion affinity chromatography. J. Biol. Chem. 263 (1988) 7211-7215
5. Hochuli E., Bannwarth W., Dobeli H., Gentz R., and Stuber D. Genetic approach to facilitate purification of recombinant proteins with a novel metal chelate absorbent. Bio/Technology 6 (1988) 1321-1325
6. Cristianini N., and Shawe-Taylor J. Support Vector Machines and Other Kernel-Based Learning Methods (2000), Cambridge University Press, Cambridge, CB2 2RU, UK
7. Scholkopf B., and Smola A.J. Learning with Kernels (2002), The MIT Press
8. Brown M., Grundy W.N., Lin D., Cristianini N., Sugnet C.W., Ares M., and Haussler D. Genetics knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci. U.S.A. 97 (2000) 262-267
9. Ramaswamy S., Tamayo P., Rifkin R., Mukherjee S., Yeang C.H., Angelo M., Ladd C., Reich M., Latulippe E., Mesirov J.P., Poggio T., Gerald W., Loda M., Lander E.S., and Golub T.R. Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. U.S.A. 98 26 (2001) 15149-15154
10. Bennett K.P., and Campbell C. Support vector machines: hype of hallelujah?. SIGKDD Explor. 2 (2000) 1-13
11. SVM and Kernel Methods MATLAB Toolbox, Insa de Rouen, http://asi.insa-rouen.fr/∼arakotom/toolbox/index.html.
12. Efron B., and Tibshirani R.J. An Introduction to the Bootstrap (1994), Chapman & Hall/CRC, Boca Raton, FL, USA
13. Efron B. Estimating the error rate of a prediction rule: improvement on cross-validation. J. Am. Stat. Assoc. 78 (1983) 316-331
14. Lebl M. Centrifugation based automated synthesis technologies. J. Assoc. Lab. Autom. 8 (2003) 30-36
15. Lebl M. New technique for high-throughput synthesis. Bioorg. Med. Chem. Lett. 9 (1999) 1305-1310
16. Hachmann J.P., and Lebl M. Alternative to piperidine in Fmoc solid phase synthesis. J. Comb. Chem. 8 (2006) 149
17. King D.S., Fields C.G., and Fields G.B. A cleavage method which minimizes side reactions following Fmoc solid phase peptide synthesis. Int. J. Pept. Prot. Res. 36 (1990) 255-266