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Why weight?

机译:为什么体重?

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摘要

Whether phylogenetic data should be differentially or equally weighted is currently debated. Further, if differential weighting is to be explored, there is no consensus among investigators as to which weighting scheme is most appropriate. Mitochondrial genome data offer a powerful tool in assessment of differential weighting schemes because taxa can be selected from which a highly corroborated phylogeny is available (so that accuracy can be assessed), and it can be assumed that different data partitions share the same history (so that gene-sorting issues are not so problematic). Using mitochondrial data from 17 mammalian genomes, we evaluated the most commonly used weighting schemes, such as successive weighting, transversion weighting, codon-based weighting, and amino acid coding, and compared them to more complex weighting schemes including a 6-parameter weighting, pseudoreplicate reweighting, and tri-level weighting. We found that the most commonly used weighting schemes perform the worst with these data. Some of the more complex schemes perform well, however, none of them is consistently superior. These results support ones biases; if one has a predilection to avoid differential weighting, these data support equally weighted parsimony and maximum likelihood. Others might be encouraged by these results to try weighting as a form of data exploration.
机译:目前正在争论是否应该对系统发育数据进行差异化或均等加权。此外,如果要研究差分加权,那么研究者之间就哪种加权方案最合适尚无共识。线粒体基因组数据提供了一种评估差异加权方案的强大工具,因为可以从中选择分类单元来获得高度确证的系统发育史(这样就可以评估准确性),并且可以假设不同的数据分区共享相同的历史记录(因此基因分类问题就不会那么麻烦了)。利用来自17个哺乳动物基因组的线粒体数据,我们评估了最常用的加权方案,例如连续加权,颠换加权,基于密码子的加权和氨基酸编码,并将它们与更复杂的加权方案(包括6参数加权)进行了比较,伪复制重新加权和三级加权。我们发现,最常用的加权方案在这些数据上表现最差。一些较复杂的方案表现良好,但是,没有一个方案始终具有优越性。这些结果支持偏见。如果一个人偏爱避免差分加权,则这些数据支持同等加权的简约性和最大似然性。这些结果可能会鼓励其他人尝试将加权作为数据探索的一种形式。

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