Movement disorders are a heterogeneous group of both common and rare neurological conditions characterized
by abnormalities of motor functions and movement patterns. This work overviews recent successes and ongoing studies of
repositioning relating to this disease group, which underscores the challenge of integrating the voluminous and heterogeneous
findings required for making suitable drug repositioning decisions. In silico drug repositioning methods hold the
promise of automated fusion of heterogeneous information sources, but the controllable, flexible and transparent incorporation
of the expertise of medicinal chemists throughout the repositioning process remains an open challenge. In support
of a more systematic approach toward repositioning, we summarize the application of a computational repurposing
method based on statistically rooted knowledge fusion. To foster the spread of this technique, we provide a step-by-step
guide to the complete workflow, together with a case study in Parkinson's disease.
Keywords: Movement disorders, Parkinson’s disease, in silico drug discovery, similarity search, data fusion, multiple kernel
learning, multi-aspect drug repositioning.
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