We address the issue of providing highly informative annotations using information revealed by the structured vocabularies of gene ontology (GO). For a target, a set of candidate terms used to infer the target's property is collected and forms a unique distribution on the GO directed acyclic graph (DAG). We propose a generic and adaptive algorithm - GOMIT, which bases on term distributions and GO hierarchical characteristics to assign correct annotations for a target. We establish a quantitative model with parameters that can be framed for optimal performance for different applications. We propose several criteria for evaluating GOMIT's performance, and conducted three experiments involving a) automated functional annotations, b) biological annotations of microarray data clusters and c) protein family GO assignments. In these experiments, we used our proposed criteria to compare GOMIT with other algorithms. Results not only reflect GOMIT's generality and adaptability, but also suggest that GOMIT is better or comparable to other works for assigning correct annotations.

We address the issue of providing highly informative and comprehensive annotations using information revealed by the structured vocabularies of gene ontology (GO). For a target, a set of candidate terms for inferring target properties is collected and form a unique distribution on the GO directed acyclic graph (DAG). We propose a novel ontology-based clustering algorithm $CLUGO, which considers GO hierarchical characteristics and the clustering of term distributions. By identifying significant groups in the distributions, CLUGO assigns comprehensive and correct annotations for a target. According to the results of experiments with automated sequence functional annotations, CLUGO represents a considerable improvement over our previous work - GOMIT in terms of recall while maintaining a similar level of precision. We conclude that given a GO candidate term distribution, CLUGO is an efficient ontology-based clustering algorithm for selecting comprehensive and correct annotations.

Fragment assembly is attracting intense research interest as a means of predicting protein structure from sequences. Success requires a good library of local structures (i.e. fragments) to serve as building blocks for whole protein reconstruction. Here, we describe a novel two-stage clustering scheme for identifying recurrent local structures and deriving fragment libraries. Parameters that represent cluster compactness, distinctiveness, and coverage were used for evaluating library quality. According to test results, our system was capable of approximating protein structures with smaller library sets at a level of accuracy comparable to or better than methods previously reported by others.