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.
This book is an inside look at the day to day activities in Leeton during the 1940‘s including the newspapers, letters from those who served in the military and personal accounts of those who remained at home. Numerous photographs are included that provide a visual made by the soldiers‘ and families‘ on their own cameras as they sought to deal with those frightening times. The story of World War II is presented from a unique perspective and will surprise many. It is enlightening to see a dedicated people committed to doing every thing they could to support the huge number of sons and daughters that volunteered and left to fight the War.
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