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Biomedical literature and databases contain important clues for the identification of potential disease bio markers.
Bio markers are potential tools that have wide range of
applications like prediction of drug efficacy/side effects, monitoring diseases
progression. Identification of bio
markers is a difficult, time consuming and costly task. This research focuses
on isolating semantic graphs formed from various biomedical databases in order
to benefit researchers in identifying bio markers.
Biomedical literature and databases contain important clues
for the identification of potential disease bio markers. However, searching
these enormous knowledge reservoirs and integrating findings across
heterogeneous sources is costly and difficult. Here we demonstrate how semantically
integrated knowledge, extracted from biomedical literature and structured
databases, can be used to automatically identify potential migraine bio
markers.
The researches used a knowledge graph containing more than
3.5 million biomedical concepts and 68.4 million relationships. Biochemical
compound concepts were filtered and ranked by their potential as bio markers
based on their connections to a sub graph of migraine-related concepts. The
ranked results were evaluated against the results of a systematic literature
review that was performed manually by migraine researchers. Weight points were
assigned to these reference compounds to indicate their relative importance.
Ranked results automatically generated by the knowledge
graph were highly consistent with results from the manual literature review.
Out of 222 reference compounds, 163 (73%) ranked in the top 2000, with 547 out
of the 644 (85%) weight points assigned to the reference compounds. For
reference compounds that were not in the top of the list, an extensive error
analysis has been performed. When evaluating the overall performance, we
obtained a ROC-AUC of 0.974.
Semantic
knowledge graphs composed of information integrated from multiple and varying
sources can assist researchers in identifying potential disease bio markers.
Journal of Biomedical Informatics
Automated extraction of potential migraine bio markers using a semantic graph
Wytze J.Vlietstra et al.
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