Ej. Moler et al., Integrating naive Bayes models and external knowledge to examine copper and iron homeostasis in S-cerevisiae, PHYSIOL GEN, 4(2), 2000, pp. 127-135
A novel suite of analytical techniques and visualization tools are applied
to 78 published transcription profiling experiments monitoring 5,687 Saccha
romyces cerevisiae genes in studies examining cell cycle, responses to stre
ss, and diauxic shift. A naive Bayes model discovered and characterized 45
classes of gene profile vectors. An enrichment measure quantified the assoc
iation between these classes and specific external knowledge defined by fou
r sets of categories to which genes can be assigned: 106 protein functions,
5 stages of the cell cycle, 265 transcription factors, and 16 chromosomal
locations. Many of the 38 genes in class 42 are known to play roles in copp
er and iron homeostasis. The 17 uncharacterized open reading frames in this
class may be involved in similar homeostatic processes; human homologs of
two of them could be associated with as yet undefined disease states arisin
g from aberrant metal ion regulation. The Met4, Met31, and Met32 transcript
ion factors may play a role in coregulating genes involved in copper and ir
on metabolism. Extensions of the simple graphical model used for clustering
to learning more complex models of genetic networks are discussed.