Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

5.5 Literature Mining Methods
at the last and least-specific of the databases. This algorithm maximises the
probability of finding a reported connection between any gene and any disease,
prompted minor disease-specific details are changed such as the identity of the
first database, which needs to be the most disease-specific of the six.
If we call "case" the code that searches for the association between queried
gene and glioblastoma in one database, and "iteration" the search loop com-
pleted from first to last database, then during each case a weighted-index is
accumulated to become, at the end of one iteration, a total weighted index, i.e.
a score of how well that particular gene did in the search for its association to
glioblastoma in the six databases. Therefore, the total weighted index repre-
sents those parameters used in the look-ups that yielded the most favourable
results, i.e. which database successfully found an association between the gene
and glioblastoma, how many hits were found, and in which part of the paper
the hits were found. In fact, I assigned a greater weight to the associations
found in the title, with respect to those found in the abstract, with respect to
those found in the contents of a paper. In designing the weight structure I also
decided to favour the associations found in the same sentence to those found
more than one sentence apart. The latter, in fact, barely carry any relevance
in the value cumulation process of the total weighted index.
In this particular application it was interesting to know whether the queried
gene was implicated in any type of cancer, especially in case the literature
did not report an association with glioblastoma. Therefore, at every iteration,
the databases are also queried with parameters that identify the answer to
the question "Is this gene implicated in any cancer?" and a separate index
from the total weighted index is determined. To calculate this cancer index
I searched for an association between the gene being queried and any of the
cancers listed in a database that I compiled for this particular application. All
the cancers listed in the National Cancer Institute A to Z List of Cancers [206]
are present in the cancer database as a unique set of names. A search was per-
formed in PubMed for every combination of queried gene symbol and cancer
name from the compiled cancer database and, if the two terms appeared in the
same sentence, this was considered an association and the cancer index for that
combination incremented by one. At the end of the search, every gene-cancer
combination possessed a cancer-index and the association was reported if the
value of such index was above an arbitrarily chosen threshold value.
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