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Simulation results with comparisons demonstrate that our proposed method performs favorably and has improved the existing work in terms of modeling performance.
Their feature extraction method performs favorably with conventional methods over remotely sensed data.
Extensive experimental results on different video sequences demonstrate that the proposed method performs favorably for detection and tracking compared with baseline methods in terms of efficiency and accuracy.
These results confirm that our weighted method performs favorably when compared to SNPs&GO.
In simulations, our method performs favorably when compared to many previously proposed approaches, including its predecessor, the sparse group lasso [Friedman et al., 2010].
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The method performed favorably when applied to real-world data.
Despite being a sequence-to-profile method, dissectHMMER performs favorably against a profile-to-profile based method-HHsuite/HHsearch. Examples of function annotation using dissectHMMER, including the function discovery of an uncharacterized membrane protein Q9K8K1_BACHD (WP_010899149.1) as a lactose/H+ symporter, are presented.
It appears that both SIFT [Ng and Henikoff, 2001] (another sequence-based method) and our unweighted method perform less favorably with accuracies of 65%and69%9%, respectively, indicating that FATHMM is somewhat the better option of the two.
However, the observed performances show that our weighted method once again performs favorably when compared to other state-of-the-art prediction methods: SNPs&GO [Calabrese et al., 2009], despite the domain-based restriction inherited from our pathogenicity weights.
Our method (in blue) performs favorably to estimating a single static network (green) or estimating each graph independently (red).
Individual methods perform more favorably for certain models, e.g. IG performs well for a 3-way model).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com