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Almost all other molecular datasets support Ecdyszoa (including Nematoda), so this must be an artifact probably due to selection.
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They observed that almost all (97.5 to 99.6%) of the molecular datasets supported the five topologies at the 95% confidence level, suggesting a lack of resolution of single gene phylogenies.
Our expanded molecular dataset supports previous suggestions by Lecompte et al. [ 13, 25, 48], that each of Myomyscus and Mastomys are paraphyletic within the Praomyini.
Elsewhere on the phylogeny, the coscoroba swan is removed from the Cygnus and emperor goose clade, and instead forms a strongly supported clade with the Cape Barren goose, a grouping that has received previous support in molecular datasets (CR1 transposon in LDH B [ 17], mitochondrial srRNA [ 29]).
Overall, molecular datasets have shown overwhelmingly strong support for placement of dibamids and gekkonids at the base of the tree, amphisbaenians with lacertoids, and iguanians with snakes and anguimorphs [ 17- 20, 23].
The data used here, the longest molecular datasets so far applied to afrobatrachian phylogeny, support the Afrobatrachia + Microhylidae hypothesis.
We have summarized our requirements for a systematic similarity threshold selection mechanism, which are: (1) ability to process large molecular datasets; (2) similarity measure independence; (3) use of structural chemical information only; (4) support the decision making process with well-defined network topology parameters.
Subsequent work by the Grass Phylogeny Working Group GPWGG) [ 2] addressed weak support within and among the grass subfamilies by making use of informative characters in seven molecular datasets along with a morphological dataset.
TCGA molecular datasets were downloaded from the Broad GDAC database (https://gdac.broadinstitute.org/).org/
Since the 1990s, platyrrhine phylogenetic studies have increasingly analyzed DNA sequences, or other molecular datasets.
Finally, when fitting support vector machines (see next section) to the data, we also applied the Recursive Feature Elimination RFEE) technique that is among the best performing variable selection methods for microarray gene expression data and other high-throughput molecular datasets [17].
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