Visual interpretation of kernel-based prediction models

Molecular Informatics

Volumn 30, Issue 9, 2011, Pages 817-826

  Hansen, Katja ^a   Baehrens, David ^a   Schroeter, Timon ^b   Rupp, Matthias ^a,c   Müller, Klaus Robert ^a,c  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b alwertconsulting   (Germany)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Confidence estimation; Domain of applicability; Kernel based learning; QSAR; QSPR

Indexed keywords

COMPUTATIONAL CHEMISTRY;

CONFIDENCE ESTIMATION; DOMAIN OF APPLICABILITY; INTERPRETABILITY; KERNEL-BASED LEARNING; MOLECULAR PROPERTIES; PREDICTION MODELLING; QSAR; QSPR; STATISTIC MODELING; VISUAL INTERPRETATION;

FORECASTING;

ARTICLE; HUMAN; KERNEL METHOD; LOGISTIC REGRESSION ANALYSIS; MUTAGENICITY; PREDICTION; PRIORITY JOURNAL; PROPORTIONAL HAZARDS MODEL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUESTIONNAIRE; RELIABILITY; STATISTICAL ANALYSIS; STATISTICAL MODEL;

EID: 80052913976     PISSN: 18681743     EISSN: 18681751     Source Type: Journal    
DOI: 10.1002/minf.201100059     Document Type: Article

References (28)

1. T. I. Oprea, Chemoinformatics in Drug Discovery, VCH, Weinheim, 2005.
2. D. Hecht, Drug Development Res. 2011, 72, 53 - 65.
3. A. Z. Dudek, T. Arodz, J. Gálvez, Comb. Chem. High Throughput Screening, 2006, 9, 213 - 228.
4. A. Schwaighofer, T. Schroeter, S. Mika, K. Hansen, A. ter Laak, P. Lienau, A. Reichel, N. Heinrich, K.- R. Müller, J. Chem. Inf. Model. 2008, 48, 785 - 796.
5. T. S. Schroeter, A. Schwaighofer, S. Mika, A. ter Laak, D. Suelzle, U. Ganzer, N. Heinrich, K.- R. Müller, ChemMedChem. 2007, 2, 1265 - 1267.
6. A. Tropsha, A. Golbraikh, Curr. Pharm. Des. 2007, 13, 3494 - 3504.
7. I. Sushko, S. Novotarskyi, R. Körner, A. Kumar Pandey, V. V. Kovalishyn, V. V. Prokopenko, I. V. Tetko, J. Chemom. 2010, 24, 202 - 208
8. A. Tropsha, in Chemoinformatics in Drug Discovery (Eds:, T. I. Oprea,), VCH, Weinheim, 2005, pp. 437 - 455
9. G. M. Maggiora, J. Chem. Inf. Model. 2006, 46, 1535.
10. W. P. Walters, B. B. Goldman, Curr. Opin. Drug Discovery Dev. 2005, 8, 329 - 333.
11. D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, K.- R. Müller, J. Mach. Learn. Res. 2010, 11, 1803 - 1831.
12. T. Schroeter, Machine Learning in Drug Discovery and Drug Design, PhD thesis, Machine Learning Dept., University of Technology Berlin 2009.
13. O. Obrezanova, M. D. Segall, J. Chem. Inf. Model. 2010, 50, 1053 - 1061.
14. C. E. Rasmussen, C. K. I. Williams, Gaussian Processes for Machine Learning, MIT Press, Cambridge, 2006.
15. M. Kuss, C. E. Ramussen, J. Mach. Learn. Res. 2005, 6, 1679 - 1704.
16. B. N. Ames, E. G. Gurney, J. A. Miller, H. Bartsch, Proc. Natl. Acad. Sci. USA 1972, 69, 3128 - 3132.
17. P. McCullagh, J. A. Nelder, Generalized Linear Models, Chapman & Hall, New York, 1990.
18. M. Chulasiri, N. Bunyapraphatsara, P. Moongkarndi, J. Toxicol. Sci. 1998, 23, 224 - 228.
19. I. Guyon, A. Elisseeff, J. Mach. Learn. Res. 2003, 3, 1157 - 1182.
20. E. Byvatov, G. Schneider, J. Chem. Inf. Comput. Sci. 2004, 44, 993 - 999.
21. L. Carlsson, E. Ahlberg Helgee, S. Boyer, J. Chem. Inf. Model. 2009, 49, 2551 - 2558.
22. R. P. Sheridan, B. P. Feuston, V. N. Maiorov, S. K. Kearsley, J. Chem. Inf. Comput. Sci. 2004, 44, 1912 - 1928.
23. T. Schroeter, A. Schwaighofer, S. Mika, A. ter Laak, D. Suelzle, U. Ganzer, N. Heinrich, K.- R. Müller, J. Comput. Aided Mol. Des. 2007, 21, 485 - 498.
24. K. Hansen, S. Mika, T. Schroeter, A. Sutter, A. ter Laak, T. Steger- Hartmann, N. Heinrich, K.- R. Müller, J. Chem. Inf. Model. 2009, 49, 2077 - 81.
25. D. M. Maniyar, I. T. Nabney, B. S. Williams, A. Sewing, J. Chem. Inf. Model. 2006, 46, 1806 - 1818.
26. J. Kolpak, P. J. Connolly, V. S. Lobanov, D. K. Agrafiotis, J. Chem. Inf. Model. 2009, 49, 2221 - 2230.
27. F. Rathke, K. Hansen, U. Brefeld, K.- R. Müller, J. Chem. Inf. Model. 2011, 51, 83 - 92.
28. B. Schölkopf, R. Herbrich, A. J. Smola, Proc. COLT ′01/EuroCOLT ′01 2001, 416 - 426.