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The less easily you could predict sequence, the better it felt.
The larger the size of the training sequence, the better the performance of the estimator (at the cost of a reduction in the system throughput).
The longer a putative target sequence, the better such random energies will be.
It is well known that the significance level is strongly influenced by the distance of the outgroup, i.e. the closer the outgroup sequence the better is the sensitivity of the RRT.
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A corollary of the hypothesis of the dilution effect is that the deeper the sequencing the better the coverage, given that most feebly represented miRNAs will be revealed by the analysis.
At the same time, the longer the sequences, the better the results with the signature method.
It is clear that the better we define the gene sequences, the better and the more comprehensively we will be able to do good research in this highly attractive model system.
The discrepancy between these results is linked to the larger dataset, which includes both 454 and capillary sequences, the better contig assembly, and the use of a more powerful tool for DTA analysis.
The GRG expresses the degree of correlation between the reference sequence (larger-the-better or smaller-the-better) and the comparison sequence [32].
This graphical representation of protein sequences has the better visibility and can reflect more information of protein sequences.
In fact, the more new correct sequences added, the better the model will become.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com