Robust parameter estimation for dynamical systems from outlier-corrupted data

Bioinformatics

Volumn 33, Issue 5, 2017, Pages 718-725

  Maier, Corinna ^a,b   Loos, Carolin ^a,b   Hasenauer, Jan ^a,b  

^a INSTITUTE OF EPIDEMIOLOGY   (Germany)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BIOLOGICAL MODEL; SIGNAL TRANSDUCTION; STATISTICAL MODEL;

ALGORITHMS; LIKELIHOOD FUNCTIONS; MODELS, BIOLOGICAL; SIGNAL TRANSDUCTION;

EID: 85020126410     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btw703     Document Type: Article

References (37)

1. Aderem,A. (2005) Systems biology: its practice and challenges. Cell, 121, 511-513.
2. Aggarwal,C.C. (2015) Outlier analysis. In: Data Mining, pp. 237-263. Springer, New York.
3. Akaike,H. (1973) Information theory and an extension of the maximum likelihood principle. In: 2nd International Symposium on Information Theory, Tsahkadsor, Armenian SSR, vol. 1, pp. 267-281. Akademiai Kiado.
4. Ben-Gal,I. (2005) Outlier detection. In: Data Mining and Knowledge Discovery Handbook, pp. 131-146. Springer, US.
5. Cao,J. et al. (2011) Robust estimation for ordinary differential equation models. Biometrics, 67, 1305-1313.
6. Chen,J.Q. et al. (2013) Absolute quantitation of endogenous proteins with precision and accuracy using a capillary western system. Anal. Biochem., 442, 97-103.
7. Fernández,C., and Steel,M.F. (1999) Multivariate student-t regression models: Pitfalls and inference. Biometrika, 86, 153-167.
8. Ghosh,D., and Vogt,A. (2012). Outliers: An evaluation of methodologies. In: Joint Statistical Meetings, pp. 3455-3460. American Statistical Association, San Diego, CA.
9. Hawkins,D.M. (1980). Identification of Outliers, vol. 11. Springer, Netherlands.
10. Hodge,V.J., and Austin,J. (2004) A survey of outlier detection methodologies. Artif. Intell. Rev., 22, 85-126.
11. Hross,S., and Hasenauer,J. (2016) Analysis of CFSE time-series data using division-, age- and label-structured population models. Bioinformatics, 32, 2321-2329.
12. Huber,P.J. et al. (1964) Robust estimation of a location parameter. Ann. Math. Stat., 35, 73-101.
13. Ideker,T. et al. (2001) A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet., 2, 343-372.
14. Jones,M., and Faddy,M. (2003) A skew extension of the t-distribution, with applications. J. R. Stat. Soc. Ser. B Stat. Methodol., 65, 159-174.
15. Kazeroonian,A. et al. (2016) CERENA: ChEmical REaction Network Analyzer - a toolbox for the simulation and analysis of stochastic chemical kinetics. PLoS ONE, 11, e0146732.
16. Kitano,H. (2002) Systems biology: a brief overview. Science, 295, 1662-1664.
17. Klipp,E. et al. (2005). Systems Biology in Practice. Wiley-VCH, Weinheim.
18. Kreutz,C. et al. (2007) An error model for protein quantification. Bioinformatics, 23, 2747-2753.
19. Kreutz,C. et al. (2012) Likelihood based observability analysis and confidence intervals for predictions of dynamic models. BMC Syst. Biol., 6, 120.
20. Lange,K.L. et al. (1989) Robust statistical modeling using the t distribution. J. Am. Statist. Assoc., 84, 881-896.
21. Motulsky,H., and Christopoulos,A. (2003). Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting. GraphPad Software Inc., San Diego, CA.
22. Niu,Z. et al. (2011) A survey of outlier detection methodologies and their applications. In: Hepu,D. et al., Artificial Intelligence and Computational Intelligence, pp. 380-387. Springer, Berlin, Heidelberg.
23. Peel,D., and McLachlan,G.J. (2000) Robust mixture modelling using the t distribution. Stat. Comput., 10, 339-348.
24. Press,W.H. et al. (1988). Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, New York, NY, USA.
25. Qiu,Y. et al. (2016) Robust estimation of parameters in nonlinear ordinary differential equation models. J. Syst. Sci. Complexity, 29, 41-60.
26. Raftery,A.E. (1999) Bayes factors and BIC. Sociol. Methods Res., 27, 411-417.
27. Ramaswamy,S. et al. (2000). Efficient algorithms for mining outliers from large data sets. In: ACM SIGMOD Record, vol. 29, pp. 427-438. ACM.
28. Raue,A. et al. (2009) Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics, 25, 1923-1929.
29. Raue,A. et al. (2013) Lessons learned from quantitative dynamical modeling in systems biology. PLoS ONE, 8, e74335.
30. Reed,W.J. (2006). The Normal-Laplace Distribution and Its Relatives, pp. 61-74. Birkhäuser Boston, Boston, MA.
31. Schelker,M. et al. (2012) Comprehensive estimation of input signals and dynamics in biochemical reaction networks. Bioinformatics, 28, i529-i534.
32. Schwarz,G. (1978) Estimating the dimension of a model. Ann. Stat., 6, 461-464.
33. Stewart,C.V. (1999) Robust parameter estimation in computer vision. SIAM Rev., 41, 513-537.
34. Swameye,I. et al. (2003) Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling. Proc. Natl. Acad. Sci. U. S. A., 100, 1028-1033.
35. Tarantola,A. (2005) Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM, Philadelphia.
36. Taylor,J., and Verbyla,A. (2004) Joint modelling of location and scale parameters of the t distribution. Stat. Model., 4, 91-112.
37. Wilks,S.S. (1938) The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Stat., 9, 60-62.