Predicting phenotype from patterns of annotation.
BACKGROUND
Predicting the outcome of specific experiments (such as the growth of a particular mutant strain in a particular medium) has the potential to allow researchers to devote resources to experiments with higher expected numbers of 'hits'.


RESULTS
We use decision trees to predict phenotypes associated with Saccharomyces cerevisiae genes on the basis of Gene Ontology (GO) functional annotations from the Saccharomyces Genome Database (SGD) and other phenotypic annotations from the Yeast Phenotype Catalog at the Munich Information Center for Protein Sequences (MIPS). We assess the methodology in three ways: (1) we use cross-validation on the phenotypic annotations listed in MIPS, and show ROC curves indicating the tradeoff between true-positive rate and false-positive rate; (2) we do a literature-search for 100 of the predicted gene-phenotype associations that are not listed in MIPS, and find evidence for 43 of them; (3) we use deletion strains to experimentally assess 61 predicted gene-phenotype associations not listed in MIPS; significantly more of these deletion strains show abnormal growth than would be expected by chance.
King
Oliver D
OD
Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, Massachusetts, 02115, USA.
Lee
Jeffrey C
JC
Dudley
Aimée M
AM
Janse
Daniel M
DM
Church
George M
GM
Roth
Frederick P
FP
D000465:Algorithms; D001185:Artificial Intelligence; D030541:Databases, Genetic; D004282:Documentation; D020869:Gene Expression Profiling / Q000379:methods*; D010363:Pattern Recognition, Automated; D010641:Phenotype; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism; D004718:Saccharomycetales / Q000145:classification / Q000235:genetics* / Q000378:metabolism