ABSTRACT
Clinicaltrials.gov [1] houses information regarding clinical trials
that are currently underway. In addition to information about
background, purpose, and design of a specific clinical trial, the
webpages also provide links to affiliated papers that can be found
in PubMed [2] (a warehouse for citations in biomedical research).
These links are explicit, but implicit links between clinical trials
and publications more than likely exist. For example, a researcher
may like to know if a given clinical trial is related to more
publications than just the ones listed on the clinical trial webpage.
This relation could be the result of similar key terms imbedded
within the clinical trial webpages and PubMed abstracts. By
using a dependent clustering algorithm [3] and a few additional
methodologies, we aim to give scientists in the biological
community insight into related clinical trials and/or other
publications of interest.
[1] ClinicalTrials.gov, A service of the U.S. National Institutes
of Health. Retrieved February 9, 2015 from:
https://clinicaltrials.gov/
[2] PubMed.gov, US National Library of Medicine, National
Institutes of Health. Retrieved Feruary 9, 2015 from:
http://www.ncbi.nlm.nih.gov/pubmed
[3] Hossain, M.S., Tadepalli, S., Watson, L.T., Davidson, I.,
Helm, R.F.& Ramakrishnan, N. (2010, July). Unifying
dependent clustering and disparate clustering for non-
homogeneous data. In proceedings of the 16th ACM SIGKDD
international conference on knowledge discovery and data
mining (pp. 593-602). ACM.
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