Bioinformatics Methods for Interpreting Toxicogenomics Data: The Role of Text-Mining

This chapter concerns the application of bioinformatics methods to the analysis of toxicogenomics data. The chapter starts with an introduction covering how bioinformatics has been applied in toxicogenomics data analysis, and continues with a description of the foundations of a specific bioinformatics method called text-mining. Next, the integration of text-mining with toxicogenomics data analysis methods is described. Four different areas in toxicogenomics where conventional bioinformatics solutions can be assisted by text-mining are described: class discovery and separation, connectivity mapping, mechanistic analysis, and identification of early predictors of toxicity. © 2014 Elsevier Inc. All rights reserved

A dictionary to identify small molecules and drugs in free text

Motivation: From the scientific community, a lot of effort has been spent on the correct identification of gene and protein names in text, while less effort has been spent on the correct identification of chemical names. Dictionary-based term identification has the power to recognize the diverse representation of chemical information in the literature and map the chemicals to their database identifiers. Results: We developed a dictionary for the identification of small molecules and drugs in text, combining information from UMLS, MeSH, ChEBI, DrugBank, KEGG, HMDB and ChemIDplus. Rule-based term filtering, manual check of highly frequent terms and disambiguation rules were applied. We tested the combined dictionary and the dictionaries derived from the individual resources on an annotated corpus, and conclude the following: (i) each of the different processing steps increase precision with a minor loss of recall; (ii) the overall performance of the combined dictionary is acceptable (precision 0.67, recall 0.40 (0.80 for trivial names); (iii) the combined dictionary performed better than the dictionary in the chemical recognizer OSCAR3; (iv) the performance of a dictionary based on ChemIDplus alone is comparable to the performance of the combined dictionary