Metrics for GO based protein semantic similarity: a systematic evaluation.
Metrics for GO based protein semantic similarity: a systematic evaluation.
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DOI：
10.1186/1471-2105-9-s5-s4
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发表时间：
2008-04-29
影响因子：
3
通讯作者：
Couto FM
中科院分区：
文献类型：
--
作者：
Pesquita C;Faria D;Bastos H;Ferreira AE;Falcão AO;Couto FM
几个语义相似性的措施已被应用到基因本体论术语注释的基因产品，提供了一个基础，其功能比较。然而，它仍然是不清楚的，这是最好的方法，在这种情况下，语义相似性，因为没有结论性的评价的各种措施。另一个问题是，电子注释是否应该或不应该被用于语义相似性计算。我们使用与序列相似性的关系作为量化其性能的手段，对基于GO的语义相似性度量进行了系统评估，并通过在存在和不存在这些注释的情况下测试这些度量来评估电子注释的影响。我们验证了语义和序列相似性之间的关系不是线性的，但可以很好地近似于一个重新标度的正态累积分布函数。鉴于大多数语义相似性度量捕获相同的行为，但分辨率不同，我们使用后者作为评估的主要标准。这项工作提供了一个基础，比较几个语义相似性的措施，并可以帮助研究人员在选择最适当的措施，他们的工作。我们发现，混合simGIC是最好的整体性能的措施，其次是Resnik的措施，使用最佳匹配平均组合方法。我们还发现，平均值和最大值组合方法是有问题的，因为两者都固有地受到被组合的项的数量的影响。我们怀疑，可能有一个直接的影响，数据循环的行为，包括电子注释的结果，从序列相似性的功能推断的结果。
Several semantic similarity measures have been applied to gene products annotated with Gene Ontology terms, providing a basis for their functional comparison. However, it is still unclear which is the best approach to semantic similarity in this context, since there is no conclusive evaluation of the various measures. Another issue, is whether electronic annotations should or not be used in semantic similarity calculations. We conducted a systematic evaluation of GO-based semantic similarity measures using the relationship with sequence similarity as a means to quantify their performance, and assessed the influence of electronic annotations by testing the measures in the presence and absence of these annotations. We verified that the relationship between semantic and sequence similarity is not linear, but can be well approximated by a rescaled Normal cumulative distribution function. Given that the majority of the semantic similarity measures capture an identical behaviour, but differ in resolution, we used the latter as the main criterion of evaluation. This work has provided a basis for the comparison of several semantic similarity measures, and can aid researchers in choosing the most adequate measure for their work. We have found that the hybrid simGIC was the measure with the best overall performance, followed by Resnik's measure using a best-match average combination approach. We have also found that the average and maximum combination approaches are problematic since both are inherently influenced by the number of terms being combined. We suspect that there may be a direct influence of data circularity in the behaviour of the results including electronic annotations, as a result of functional inference from sequence similarity.