Extraction of the pharmacological treatment features in literature-based discovery by applying cluster analysis.

Literature-based discovery (LBD) facilitates the extraction of hidden relationships between a disease and chemical substances. As a methodology of LBD, we had previously proposed to apply cluster analysis to analyze the intermediate concepts between them. In this study, we compared the ranks of chemical substances predicted by our methodology to the original rank so as to validate possibilities for extraction of the heterogeneity in the relationships.

Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling.

The Carcinogenicity Reliability Database (CRDB) was constructed by collecting experimental carcinogenicity data on about 1,500 chemicals from six sources, including IARC, and NTP databases, and then by ranking their reliabilities into six unified categories. A wide variety of 911 organic chemicals were selected from the database for QSAR modeling, and 1,504 kinds of different molecular descriptors were calculated, based on their 3D molecular structures as modeled by the Dragon software. Positive (carcinogenic) and negative (non-carcinogenic) chemicals containing various substructures were counted using atom and functional group count descriptors, and the statistical significance of ratios of positives to negatives was tested for those substructures.