Interrogating health-related public databases from a food toxicology perspective: Computational analysis of scoring data

• Published in: Food and chemical toxicology Vol. 49; no. 11; pp. 2830 - 2840
• Main Authors: Maddah, F., Soeria-Atmadja, D., Malm, P., Gustafsson, M.G., Hammerling, U.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-11-2011; Elsevier
• Subjects: Animals; Biological and medical sciences; Cluster Analysis; Context-specific rating; Databases, Factual; Food - adverse effects; food contamination; Food Contamination - analysis; Food toxicology; information sources; Internet; Mammals; Medical sciences; Multi-variate data analysis; pesticides; Public databases; risk assessment; Toxicology; Context-specific rating; Public databases; Multi-variate data analysis; Food toxicology; Data analysis; Health; Information access; Perspective; Toxicology; Database; Public information; Internet; Food; Computer aided analysis
• DOI: 10.1016/j.fct.2011.08.002
• EISSN: 1873-6351
• ISSN: 0278-6915; 1873-6351

► This study involves the comparative evaluation of a large array of health-related databases, using 18 test substances. ► We have applied various uni- and multi-variate data analysis methods, including cluster analysis, to process scoring data. ► A special index was devised to arrive at context-related rating of the databases. ► Cluster analysis revealed groups of databases with overall shared characteristics.
Over the last 15years, an expanding number of databases with information on noxious effects of substances on mammalian organisms and the environment have been made available on the Internet. This set of databases is a key source of information for risk assessment within several areas of toxicology. Here we present features and relationships across a relatively wide set of publicly accessible databases broadly within toxicology, in part by clustering multi-score representations of such repositories, to support risk assessment within food toxicology. For this purpose 36 databases were each scrutinized, using 18 test substances from six different categories as probes. Results have been analyzed by means of various uni- and multi-variate statistical operations. The former included a special index devised to afford context-specific rating of databases across a highly heterogeneous data matrix, whereas the latter involved cluster analysis, enabling the identification of database assemblies with overall shared characteristics. One database – HSDB – was outstanding due to rich and qualified information for most test substances, but an appreciable fraction of the interrogated repositories showed good to decent scoring. Among the six chosen substance groups, Food contact materials had the most comprehensive toxicological information, followed by the Pesticides category.