The way to apply Bayesian modeling is illustrated to validate a ligand-binding assay such as ELISA. Hierarchical non-linear model and the associated predictive distribution of back-calculated responses allow quantifying the total uncertainty of every future measurement with the assays, through the use of precision and risk profiles. It is also shown how the obtained posterior distribution of the parameters can be used as prior for new the calibration curves during routine
In the past several decades drug development has come a long way in testing assays of medicinal comp...
In pharmaceutical and biomedical industries, quantitative analytical methods such as HPLC play a key...
International audienceThis paper addresses the question of biomarker discovery in proteomics. Given ...
For ligan-binding assay, different parameters are controlled in routine. In a development phase, we ...
Motivation: Immunoassays are primary diagnostic and research tools throughout the medical and life s...
Bayesian methods for estimating dose response curves in quantal bioassay are studied. A linearized m...
Analytical quantitative methods are widely used to quantify analytes of interest, for instance in ph...
This article presents Bayesian bootstrap techniques for risk assessment in bioassays and development...
Large databases of routinely collected data are a valuable source of information for detecting poten...
Through an example, the way to apply Design of Experiments and the Bayesian modeling to develop robu...
We develop a Bayesian nonparametric mixture modeling framework for quantal bioassay settings. The ap...
Statistical analysis is a vital component of an assay. In an immunoassay, the diagnosis of an illnes...
International audienceProteochemometric (PCM) is an approach for bioactivity predictive modeling whi...
In toxicology screening (forensic, food-safety), due to several analytical errors (e.g., retention t...
The enzyme-linked immunosorbent assay (ELISA) is a powerful quantitative tool with predictive abilit...
In the past several decades drug development has come a long way in testing assays of medicinal comp...
In pharmaceutical and biomedical industries, quantitative analytical methods such as HPLC play a key...
International audienceThis paper addresses the question of biomarker discovery in proteomics. Given ...
For ligan-binding assay, different parameters are controlled in routine. In a development phase, we ...
Motivation: Immunoassays are primary diagnostic and research tools throughout the medical and life s...
Bayesian methods for estimating dose response curves in quantal bioassay are studied. A linearized m...
Analytical quantitative methods are widely used to quantify analytes of interest, for instance in ph...
This article presents Bayesian bootstrap techniques for risk assessment in bioassays and development...
Large databases of routinely collected data are a valuable source of information for detecting poten...
Through an example, the way to apply Design of Experiments and the Bayesian modeling to develop robu...
We develop a Bayesian nonparametric mixture modeling framework for quantal bioassay settings. The ap...
Statistical analysis is a vital component of an assay. In an immunoassay, the diagnosis of an illnes...
International audienceProteochemometric (PCM) is an approach for bioactivity predictive modeling whi...
In toxicology screening (forensic, food-safety), due to several analytical errors (e.g., retention t...
The enzyme-linked immunosorbent assay (ELISA) is a powerful quantitative tool with predictive abilit...
In the past several decades drug development has come a long way in testing assays of medicinal comp...
In pharmaceutical and biomedical industries, quantitative analytical methods such as HPLC play a key...
International audienceThis paper addresses the question of biomarker discovery in proteomics. Given ...