Information-theoretic evaluation of predicted ontological annotations.
Information-theoretic evaluation of predicted ontological annotations.
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DOI：
10.1093/bioinformatics/btt228
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发表时间：
2013-07-01
期刊：
影响因子：
--
通讯作者：
Radivojac P
中科院分区：
文献类型：
--
作者：
Clark WT;Radivojac P
动机：开发有效的方法来预测本体学注释是计算生物学的重要目标，尽管已经提出了各种算法，但蛋白质功能预测和疾病基因的优先级得到了广泛的认可。生物医学本体论的结构以及基因和基因产物的有偏见或不完整的实验注释引起的问题。
结果：我们提出了一个信息理论框架，以评估计算蛋白功能预测的性能。仍然存在不确定性，可以看作是精确的信息理论类似物，我们提出了一个单一的统计量，称为语义距离，可以通过分析三种方法来评估我们的方法。基因本体术语的蛋白质功能预测指标，并提供了它解决当前使用指标的几个弱点。
联系人：predrag@indiana.edu
补充信息：可以在线生物信息学上获得补充数据。
Motivation: The development of effective methods for the prediction of ontological annotations is an important goal in computational biology, with protein function prediction and disease gene prioritization gaining wide recognition. Although various algorithms have been proposed for these tasks, evaluating their performance is difficult owing to problems caused both by the structure of biomedical ontologies and biased or incomplete experimental annotations of genes and gene products. Results: We propose an information-theoretic framework to evaluate the performance of computational protein function prediction. We use a Bayesian network, structured according to the underlying ontology, to model the prior probability of a protein’s function. We then define two concepts, misinformation and remaining uncertainty, that can be seen as information-theoretic analogs of precision and recall. Finally, we propose a single statistic, referred to as semantic distance, that can be used to rank classification models. We evaluate our approach by analyzing the performance of three protein function predictors of Gene Ontology terms and provide evidence that it addresses several weaknesses of currently used metrics. We believe this framework provides useful insights into the performance of protein function prediction tools. Contact: predrag@indiana.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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