Your English writing platform
Discover LudwigSuggestions(2)
Exact(60)
The predictions were performed using LIBSVM.
In those qualified stations, 217 correct intensity predictions were performed which is 77%.
Protein predictions were performed using FGENESH (http://linux1.softberry.com/berry.phtml?topic=fgenes_plus&group=programs&subgroup=gfs).
The predictions were performed with k-NN algorithm executed using data streams.
The predictions were performed with the last version of AFGROW and NASGRO 3.0 software.
Class A and Class C predictions were performed via numerical and analytical approaches.
Finite element-based computational wear predictions were performed to 5 million gait cycles using both force- and displacement-controlled inputs.
Gene functional predictions were performed as follows.
Secondary structure predictions were performed by Jpred server [14].
The microRNA target predictions were performed using the program PITA[31].
Initial coding sequence (CDS) predictions were performed using Orpheus [16], Glimmer2 [17], and EasyGene [18] software.
Write better and faster with AI suggestions while staying true to your unique style.
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