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Multi-Assay-Based Compound Prioritization via Assistance Utilization: A Machine Learning Framework
journal contribution
posted on 2017-02-24, 00:00 authored by Junfeng Liu, Xia NingEffective prioritization
of chemical compounds that show promising
bioactivities from compound screenings represents a first critical
step toward identifying successful drug candidates. Current development
on computational approaches for compound prioritization is largely
focused on devising advanced ranking algorithms that better learn
the ordering among compounds. However, such methodologies are fundamentally
limited by the scarcity of available data, particularly when the screenings
are conducted at a relatively small scale over known promising compounds.
Instead, in this work, we explore the structures of bioassay space
and leverage such structures to improve ranking performance of an
existing strong ranking algorithm. This is done by identifying assistance bioassays and assistance compounds
intelligently and leveraging such assistance within the existing ranking
algorithm. By leveraging the assistance bioassays and assistance compounds,
the data scarcity can be properly compromised. Along this line, we
develop a suite of assistance bioassay selection methods and assistance
compound selection methods. Our experiments demonstrate an overall
8.34% improvement on the ranking performance over the state of the
art.