Biomedical Engineering Reference
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miss important search results if they do not search for each of the
various names, synonyms, and symbols used to represent a given entity
of interest.
As an example of the diffi culties associated with text-based search systems
for biomedical research, let us imagine the scenario that a user wants to
search for all of the information available for the gene ABL1. First, ABL1
has many names and symbols such as ABL1, c-abl, and V-abl Abelson
murine leukemia viral oncogene homolog 1. Second, ABL1 can be both a
gene and a protein (the translation of the gene). Third, ABL1 is a gene
found in many species including human, mouse, rat, and primate. So, our
hypothetical user would start in a text-based search system by typing
ABL1 and they would receive a large set of document links as a result.
That search would only be run across text documents (i.e. unstructured
data sources) and thus the user would not be provided with any
information about the gene from structured databases or other data
sources. Additionally, how does the user know that they have found all
documents relevant to ABL1 and all of its synonyms, symbols, and
identifi ers instead of only those that mention ABL1? Further, how do
they refi ne their search to results matching the gene versus protein version
of ABL1 and also the right species? Confronted with this set of challenges,
it is often the case that the user simply does not address any of these
issues and instead just accepts the results provided and attempts to sift
through large quantities of results in order to identify those documents
most relevant to their interests. In addition to the diffi culties associated
with fi nding all pertinent information for a given entity, it is often the
case that the user is not only interested in the single entity but also for
other entities with which it might be associated. For example, for a given
gene product, like ABL1 protein, there may be compounds that are
selective for it, diseases in which it is indicated, and clinical trials that
might target it. All of the associations for a user's searched entity (e.g.
ABL1) simply are not provided by text-based search systems, and instead
the user once again must read through all resulting documents in order to
try to glean associations for their search term. By forcing a user to
manually read through large sets of documentation, critical properties
of entities and knowledge about associations between entities that might
be highly valuable to their research can be easily missed. To sum up, text-
based search systems, although valuable tools, lack a range of attributes
that would be useful in scientifi c and related disciplines in which a user is
interested in searching for and analyzing structured data representations
of entities and their associations.
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