Information-Theoretic Evaluation for Computational by Wyatt Travis Clark

By Wyatt Travis Clark

The improvement of potent tools for the prediction of ontological annotations is a vital target in computational biology, but comparing their functionality is hard as a result of difficulties attributable to the constitution of biomedical ontologies and incomplete annotations of genes. This paintings proposes an information-theoretic framework to guage the functionality of computational protein functionality prediction. A Bayesian community is used, established in accordance with the underlying ontology, to version the earlier chance of a protein's functionality. The strategies of incorrect information and final uncertainty are then outlined, that may be obvious as analogs of precision and remember. ultimately, semantic distance is proposed as a unmarried statistic for rating type versions. The procedure is evaluated by way of reading 3 protein functionality predictors of gene ontology phrases. The paintings addresses a number of weaknesses of present metrics, and gives useful insights into the functionality of protein functionality prediction tools.

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Under the ontology-free model a large fraction of annotations could have high information content associated with them, regardless of how detailed the annotations are, if only a few data points have the exact same annotation. 3 Two-Dimensional Plots In order to assess how each metric evaluated the performance of the four prediction methods, we generated two-dimensional plots. 3 shows the performance of each predictor using precision/recall and ru–mi curves, as well as their weighted variants. The performance of the GO/Swiss-Prot annotation is represented as a single point because it compares two databases of experimental annotations where predictions are all binary and do not have associated scores.

Furthermore, 41 % of proteins are annotated with its child “protein binding" as a leaf term, and 26 % are annotated with it as their sole leaf term. Such annotations, which are clearly a consequence of high-throughput experiments, present a significant difficulty in method evaluation. Previously, we showed that the distribution of leaf terms in protein annotation graphs exhibits scale-free tendencies [1]. Here, we also analyzed the average number of leaf terms per protein and compared it with the information content of that protein.

79(7), 2086–2096 (2011) 2. : Semantic similarity based on corpus statistics and lexical taxonomy. In international conference on research in computational linguistics, 19–33, 1997 3. : An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, pp. 296–304. Morgan Kaufmann (1998) 4. : Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics 19(10), 1275–1283 (2003) References 41 5.

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