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Background  Toxicogenomics studies often profile gene expression from assays involving multiple doses and time points. The dose- and time-dependent pattern is of great importance to assess toxicity but computational approaches are lacking to effectively utilize this characteristic in toxicity assessment. Topic modeling is a text mining approach, but may be used analogously in toxicogenomics due to the similar data structures between text and gene dysregulation.    Results  Topic modeling was applied to a very large toxicogenomics dataset containing microarray gene expression data from >15,000 samples associated with 131 drugs tested in three different assay platforms (i.e.,  in vitro  assay,  in vivo  repeated dose study and  in vivo  single dose experiment) with a design including multiple doses and time points. A set of “topics” which each consist of a set of genes was determined, by which the varying sensitivity of three assay systems was observed. We found that the drug-dependent effect was more pronounced in the two  in vivo  systems than the  in vitro  system, while the time-dependent effect was most strongly reflected in the  in vitro  system followed by the single dose study and lastly the repeated dose experiment. The dose-dependent effect was similar across three assay systems. Although the results indicated a challenge to extrapolate the  in vitro  results to the  in vivo  situation, we did notice that, for some drugs but not for all the drugs, the similarity in gene expression patterns was observed across all three assay systems, indicating a possibility of using  in vitro  systems with careful designs (such as the choice of dose and time point), to replace the  in vivo  testing strategy. Nonetheless, a potential to replace the repeated dose study by the single-dose short-term methodology was strongly implied.    Conclusions  The study demonstrated that text mining methodologies such as topic modeling provide an alternative method compared to traditional means for data reduction in toxicogenomics, enhancing researchers’ capabilities to interpret biological information.

Of text and gene – using text mining methods to uncover hidden knowledge in toxicogenomics