PMID:17683567

Citation	Andorf, C, Dobbs, D and Honavar, V (2007) Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach. BMC Bioinformatics 8:284
Abstract	Incorrectly annotated sequence data are becoming more commonplace as databases increasingly rely on automated techniques for annotation. Hence, there is an urgent need for computational methods for checking consistency of such annotations against independent sources of evidence and detecting potential annotation errors. We show how a machine learning approach designed to automatically predict a protein's Gene Ontology (GO) functional class can be employed to identify potential gene annotation errors.
Links	PubMed PMC1994202 Online version:10.1186/1471-2105-8-284
Keywords	Algorithms; Amino Acid Sequence; Artificial Intelligence; Chromosome Mapping/methods; Molecular Sequence Data; Pattern Recognition, Automated/methods; Proteins/chemistry; Sensitivity and Specificity; Sequence Analysis, Protein/methods
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