Automated Protocol for Large-Scale Modeling of Gene
Expression Data
Version 2 2016-11-11, 20:19
Version 1 2016-11-10, 14:23
Posted on 2016-10-31 - 00:00
With the continued
rise of phenotypic- and genotypic-based screening
projects, computational methods to analyze, process, and ultimately
make predictions in this field take on growing importance. Here we
show how automated machine learning workflows can produce models that
are predictive of differential gene expression as a function of a
compound structure using data from A673 cells as a proof of principle.
In particular, we present predictive models with an average accuracy
of greater than 70% across a highly diverse ∼1000 gene expression
profile. In contrast to the usual in silico design paradigm, where
one interrogates a particular target-based response, this work opens
the opportunity for virtual screening and lead optimization for desired
multitarget gene expression profiles.
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Hall, Michelle Lynn; Calkins, David; Sherman, Woody (2016). Automated Protocol for Large-Scale Modeling of Gene
Expression Data. ACS Publications. Collection. https://doi.org/10.1021/acs.jcim.6b00260
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AUTHORS (3)
MH
Michelle Lynn Hall
DC
David Calkins
WS
Woody Sherman