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The clinoform flattening observed in the Adriatic sequences contrasts with clinoform and depositional shelf break development that is characteristic of both conceptual and numerical models of sequences, including the one used here.
Our goal for the transposon study was to determine if the same pattern of improvement is seen for profile HMM models of sequences generated by natural biological processes as that seen with data simulated from profile HMMs.
On the other hand, it was also shown [ 17] that pursuing discriminant analysis using representations that are not constrained by predefined scales or succession orders, even when those scales are systematically screened such as in variable length Markov models [ 18- 20], leads to more accurate models of sequences.
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Prior to performing the ML analyses, the models of sequence evolution were determined in Modeltest 3.7 [62].
For each alignment, we performed two searches using different models of sequence evolution.
The TBC uses models of sequence evolution and performs resampling of sequence alignments, and does not require phylogenetic tree estimation.
This approach provides a statistical framework for evaluating explicit models of sequence evolution that are of biological interest.
Best-fitting models of sequence evolution were determined using Modeltest 3.7 [68] for ML reconstructions and MrModeltest 2.2 [69] for BA.
No models of sequence change are explicitly invoked.
We partitioned models of sequence evolution by codon, and also by gene when multiple genes were used, applying models of sequence evolution selected by DT ModSel [ 46].
This trend may be due to the models of sequence evolution utilized for estimating sequence divergence.
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