  We present a novel classification-based algorithm called GeneClass for
learning to predict gene regulatory response. Our approach is motivated by the
hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can
learn a decision rule for predicting whether a gene is up- or down-regulated in
a particular experiment based on (1) the presence of binding site subsequences
(``motifs'') in the gene's regulatory region and (2) the expression levels of
regulators such as transcription factors in the experiment (``parents''). Thus
our learning task integrates two qualitatively different data sources:
genome-wide cDNA microarray data across multiple perturbation and mutant
experiments along with motif profile data from regulatory sequences. Rather
than focusing on the regression task of predicting real-valued gene expression
measurements, GeneClass performs the classification task of predicting +1 and
-1 labels, corresponding to up- and down-regulation beyond the levels of
biological and measurement noise in microarray measurements. GeneClass uses the
Adaboost learning algorithm with a margin-based generalization of decision
trees called alternating decision trees. In computational experiments based on
the Gasch S. cerevisiae dataset, we show that the GeneClass method predicts up-
and down-regulation on held-out experiments with high accuracy. We explore a
range of experimental setups related to environmental stress response, and we
retrieve important regulators, binding site motifs, and relationships between
regulators and binding sites that are known to be associated to specific stress
response pathways. Our method thus provides predictive hypotheses, suggests
biological experiments, and provides interpretable insight into the structure
of genetic regulatory networks.
