The ADESSA system, a real-time decision support service for detecting or monitoring adverse drug events, uses a regular expression and dictionary-based method to extract ADRs [ 15, 16].
Once the dictionary is complete, the python code reads in the XML tags and uses a regular expression to determine terms and tokens within the XML tags that need to be removed from each record.
We use MotifScanner from the inclusive package by Thijs et al. (2002) to scan sequences for the best matching occurrence of a PWM; in case of an oligomer, we use a regular expression pattern with decreasing discriminative power to find the closest match.
The SLiMSearch programme uses a regular expression, annotated within the ELM resource, to identify potential SLiMs and assigns a Relative Local Conservation (RLC) score of the residues based on a multiple alignment of the sequence and its orthologues [see Davey et al. (2011) for details].
For example the term "fever" used a regular expression of "fe?ve?r(([iey]sh)|y|s?)" to detect words stemmed from "fever" including "fevers", "feverish" and "fevery" but to also account for abbreviations to a word stem of "fver", "fevr" or "fvr" as well as misspellings such as "feverysh".
