Information-Theoretic Evaluation for Computational Biomedical Ontologies

Nonfiction, Computers, Advanced Computing, Computer Science, Programming, Science & Nature, Science
Cover of the book Information-Theoretic Evaluation for Computational Biomedical Ontologies by Wyatt Travis Clark, Springer International Publishing
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Author: Wyatt Travis Clark ISBN: 9783319041384
Publisher: Springer International Publishing Publication: January 9, 2014
Imprint: Springer Language: English
Author: Wyatt Travis Clark
ISBN: 9783319041384
Publisher: Springer International Publishing
Publication: January 9, 2014
Imprint: Springer
Language: English

The development of effective methods for the prediction of ontological annotations is an important goal in computational biology, yet evaluating their performance is difficult due to problems caused by the structure of biomedical ontologies and incomplete annotations of genes. This work proposes an information-theoretic framework to evaluate the performance of computational protein function prediction. A Bayesian network is used, structured according to the underlying ontology, to model the prior probability of a protein's function. The concepts of misinformation and remaining uncertainty are then defined, that can be seen as analogs of precision and recall. Finally, semantic distance is proposed as a single statistic for ranking classification models. The approach is evaluated by analyzing three protein function predictors of gene ontology terms. The work addresses several weaknesses of current metrics, and provides valuable insights into the performance of protein function prediction tools.

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The development of effective methods for the prediction of ontological annotations is an important goal in computational biology, yet evaluating their performance is difficult due to problems caused by the structure of biomedical ontologies and incomplete annotations of genes. This work proposes an information-theoretic framework to evaluate the performance of computational protein function prediction. A Bayesian network is used, structured according to the underlying ontology, to model the prior probability of a protein's function. The concepts of misinformation and remaining uncertainty are then defined, that can be seen as analogs of precision and recall. Finally, semantic distance is proposed as a single statistic for ranking classification models. The approach is evaluated by analyzing three protein function predictors of gene ontology terms. The work addresses several weaknesses of current metrics, and provides valuable insights into the performance of protein function prediction tools.

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