A probabilistic framework to predict protein function from interaction data integrated with semantic knowledge

BMC Bioinformatics

Table 2 Function prediction results for unknown proteins

unknown	predicted function		confidence
	ID in MIPS	description
YAL027W	02.16.01	alcohol fermentation	95.1
YAL053W	01.05	C-compound and carbohydrate metabolism	34.3
YAR027W	20.01.27	drug/toxin transport	66.0
YBL046W	01.02	nitrogen, sulfur or selenium metabolism	34.2
YBL046W	14.07.03	modification by phosphorylation/dephosphorylation	37.0
YCL028W	01.03.07	deoxyribonucleotide metabolism	62.7
YFL042C	02.16.01	alcohol fermentation	46.3
YGL230C	20.01.11	amine/polyamine transport	32.7
YGR163W	14.13.04	lysosomal and vacuolar protein degradation	59.9
YGR163W	20.01.01	ion transport	64.1
YGR163W	34.01.01	homeostasis of cations	115.2
YHL042W	14.07.02.01	glycosylation/deglycosylation	49.2
YHR105W	14.07.02.01	glycosylation/deglycosylation	49.2
YHR140W	20.01.27	drug/toxin transport	66.0
YJL058C	01.04	phosphate metabolism	36.0
YJL058C	01.06	lipid, fatty acid and isoprenoid metabolism	215.7
YJL058C	42.04	cytoskeleton/structural proteins	42.6
YJL122W	10.03.01.01	mitotic cell cycle	34.4
YLR376C	10.03.02	meiosis	36.6
YLR376C	10.03.04	nuclear or chromosomal cycle	37.1
YKL065C	20.01.11	amine/polyamine transport	50.3
YKL065C	32.05.01	resistance proteins	51.9
YPL264C	01.20.19.01	metabolism of porphyrins	54.9

For each unknown protein, our probabilistic approach predicted a set of functions with prediction confidence, log λ. The prediction results, whose confidence is greater than 32, were listed. A protein can have more than one function predicted with different confidence rates.

ISSN: 1471-2105