1 research outputs found
Emerging Pattern Mining To Aid Toxicological Knowledge Discovery
Knowledge-based systems for toxicity
prediction are typically based
on rules, known as structural alerts, that describe relationships
between structural features and different toxic effects. The identification
of structural features associated with toxicological activity can
be a time-consuming process and often requires significant input from
domain experts. Here, we describe an emerging pattern mining method
for the automated identification of activating structural features
in toxicity data sets that is designed to help expedite the process
of alert development. We apply the contrast pattern tree mining algorithm
to generate a set of emerging patterns of structural fragment descriptors.
Using the emerging patterns it is possible to form hierarchical clusters
of compounds that are defined by the presence of common structural
features and represent distinct chemical classes. The method has been
tested on a large public <i>in vitro</i> mutagenicity data
set and a public hERG channel inhibition data set and is shown to
be effective at identifying common toxic features and recognizable
classes of toxicants. We also describe how knowledge developers can
use emerging patterns to improve the specificity and sensitivity of
an existing expert system