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Information-Theoretic Evaluation for Computational Biomedical Ontologies



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Éditeur :

Springer


Collection :

SpringerBriefs in Computer Science

Paru le : 2014-01-09



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Description
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.
Pages
46 pages
Collection
SpringerBriefs in Computer Science
Parution
2014-01-09
Marque
Springer
EAN papier
9783319041377
EAN EPUB
9783319041384
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
4
Taille du fichier
891 Ko
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Prix
52,74 €

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