An algorithm for data quality assessment in predictive toxicology

Lack of the quality of the information that is integrated from heterogeneous sources is an important issue in many scientific domains. In toxicology the importance is even greater since the data is used for Quantitative Structure Activity Relationship (QSAR) modeling for prediction of chemical toxicity of new compounds. Much work has been done on QSARs but little attention has been paid to the quality of the data used. The underlying concept points to the absence of the quality criteria framework in this domain. This paper presents a review on some of the existing data quality assessment methods in various domains and their relevance and possible application to predictive toxicology, highlights number of data quality deficiencies from experimental work on internal data and also proposes some quality metrics and an algorithm for assessing data quality concluded from the results.