Despite many migraine-specific treatments that became available over the past 5 years, many patients still suffer from debilitating migraine. Emerging and future directions of migraine research and treatment should consider different aspects including revising the headache diagnostic criteria to reflect disease burden and prognosis, developing biomarkers, including genetic, serum, imaging, and deep phenotyping biomarkers to facilitate personalized medicine for headache treatment. Additionally, research should also emphasize identifying novel treatment targets for drug development. In this chapter, we provide an overview of current studies and controversies in the diagnosis of migraine and available research on potential migraine biomarkers. We also discuss potential treatment targets for migraine, including CGRP, PACAP, orexin, non-μ opioid receptors, nitric oxide, BKCa channel, KATP channel, amylin, TRP channels, prolactin, PAR-2, and other potential targets.

Biomedical literature and databases contain important clues for the identification of potential disease biomarkers. 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 biomarkers.
We 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 biomarkers based on their connections to a subgraph 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 biomarkers.