Exact(3)
Comparing with Figure 2, we find that for eight of these factors the DWM method greatly outperforms the PWM method: the exceptions are ABF1 and CBF1.
Experimental results indicate that our threading method greatly outperforms the best profile-based method HHpred and all the top CASP8 servers on low-homology proteins.
Experimental results indicate that our method greatly outperforms the best profile-based method HHpred and the top CASP8 servers on low-homology proteins.
Similar(57)
This method greatly outperformed the original MSM.
The results can be found in Table 3. From the results, we can find that the accuracies of deep learning hashing method greatly outperform the traditional hashing methods, which demonstrate the power of deep learning technology in binary code learning.
In a cross-validation experiment in yeast, mouse and human, our method greatly outperformed previous state-of-the-art function prediction algorithms in predicting sparsely annotated functions, without sacrificing the performance on labels with sufficient information.
However, with face alignment on the database, it is believed that the proposed method will greatly outperform all methods reported so far in the literature.
Extensive tests indicate that HubAlign greatly outperforms several popular methods in terms of both accuracy and efficiency, especially in detecting functionally similar proteins.
Using a simulation, we further validated that SWEEP greatly outperforms current filtering methods, retaining a high percentage of true SNPs relative to homeologous polymorphisms (65%99%% depending on coverage) compared to other methods (2%–8%).
We have tested HubAlign on both prokaryotic and eukaryotic PPI networks, showing that HubAlign greatly outperforms several popular methods such as IsoRank, MI-GRAAL, GHOST and PISwap in terms of both alignment accuracy and running time.
For example, if only one shared GO term is required, HubAlign greatly outperforms the second best method GHOST for five of nine alignments (i.e., human fly, fly yeast, mouse worm, mouse fly and mouse yeast) and slightly outperforms GHOST for the remaining four alignments.
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