Premium
CODE: a deconvolution program implementing a regularization method of deconvolution constrained to non‐negative values. Description and pilot evaluation
Author(s) -
Hovorka Roman,
Chappell Michael J.,
Godfrey Keith R.,
Madden Francis N.,
Rouse Martin K.,
Soons Paul A.
Publication year - 1998
Publication title -
biopharmaceutics and drug disposition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.419
H-Index - 58
eISSN - 1099-081X
pISSN - 0142-2782
DOI - 10.1002/(sici)1099-081x(199801)19:1<39::aid-bdd73>3.0.co;2-m
Subject(s) - deconvolution , regularization (linguistics) , blind deconvolution , code (set theory) , algorithm , computer science , function (biology) , least squares function approximation , mathematical optimization , mathematics , statistics , artificial intelligence , set (abstract data type) , evolutionary biology , estimator , programming language , biology
A regularization method of deconvolution constrained to non‐negative values is described. The method gives smooth estimates of the input function whilst providing a feasible fit (in terms of least squares) to measurements. A description of the program CODE (constrained deconvolution) which implements the method is given. A new methodology for a pilot evaluation of deconvolution programs is also proposed. The methodology is based on synthetic data. It employs a variety of shapes of the input function, low (1%) and high (15%) values of the measurement error, and incorporates primary (accuracy) and secondary (bias) performance measures. The performance of CODE is evaluated and it is suggested that CODE provides estimates of the input function with acceptable accuracy. © 1997 John Wiley & Sons, Ltd.