Name
Abstract | ||
|---|---|---|
This paper addresses the problem of estimating the expressions or concentrations of proteins from measurements obtained from protein arrays and illustrates the methodology on lysate microarray data. With several families of parametric models we design a number of algorithms for the estimation of a highly nonlinear calibration curve as well as the concentrations themselves. The model families include polynomial and sigmoidal nonlinearities for the calibration curve and homoscedastic or heteroscedastic models for the noise. The accuracy of the estimation methods is tested on simulated data and applied to real lysate array data. The results are generally very good, provided that strongly nonlinear models are used. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/TSP.2006.873719 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
arrays,maximum likelihood estimation,polynomials,proteins,Monte Carlo simulations,heteroscedastic models,homoscedastic models,lysate microarray data,maximum-likelihood estimation,polynomial,protein arrays,protein expressions,sigmoidal nonlinearities,Heteroscedastic noise,maximum-likelihood estimation,microarray data,model order selection,nonlinear estimation,proteins | Mathematical optimization,Nonlinear system,Parametric model,Polynomial,Expression (mathematics),Identifiability,Homoscedasticity,Calibration curve,Mathematics,Sigmoid function | Journal |
Volume | Issue | ISSN |
54 | 6 | 1053-587X |
Citations | PageRank | References |
5 | 1.28 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| I. Tabus | 1 | 87 | 10.32 |
| Hategan, A. | 2 | 5 | 1.28 |
| Mircean, C. | 3 | 5 | 1.28 |
| Jorma Rissanen | 4 | 1665 | 798.14 |