Information-Theoretic Evaluation for Computational Biomedical Ontologies (Paperback, 2014)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Computer Science
Release date:	2014
First published:	2014
Authors:	Wyatt Travis Clark
Dimensions:	235 x 155 x 8mm (L x W x T)
Format:	Paperback
Pages:	46
Edition:	2014
ISBN-13:	978-3-319-04137-7
Categories:	Books > Medicine > Pre-clinical medicine: basic sciences > Medical genetics Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Pattern recognition Books > Medicine > General issues > Medical equipment & techniques > General Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > General
LSN:	3-319-04137-1
Barcode:	9783319041377

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