As noted in the introduction, a close affinity between Anhingidae and Phalacrocoracidae has generally been accepted as uncontroversial, with the exception of several mitochondrial datasets that recovered an unconventional sister taxon relationship between Anhingidae and Sulidae [2], [17].
It performs better than other competing methods on several benchmark datasets that include over 1,200 enzyme structures.
The results demonstrate that the proposed method (EXIA2) could outperform the existing methods on several benchmark datasets that include over 1,200 enzyme structures.
In the Results section (below) we used several sample datasets that were constructed using the following sources: SRS [ 30] was used for mapping connections between different databases, the String database [ 31] for finding protein-protein interactions, OMIM for disease information, PDB for structure information [ 32], GO for the Gene Ontology information [ 18].
Empirical evaluation on several datasets shows that MoRFpred outperforms related methods: α-MoRF-Pred that predicts α-MoRFs and ANCHOR which finds disordered regions that become ordered when bound to a globular partner.
It is clear from the data that YNG/ING is poorly represented in several datasets, and that different methods produced different biases in which ING is detected.
Experimental results on several datasets demonstrate that our model acquires better performance over the state-of-the-art methods with respect to occlusion handling and attain higher alignment accuracy.
With respect to the choice of one or more reference complexes, the high standard deviations for the average TIFP performance for several datasets suggest that the selection of a suitable structure is crucial for scoring success.
In this section, we present several relatively small datasets that illustrate how Arena3D can be used to gain insight into biological networks; however we refer interested readers to, where they can download the program together with several example datasets.
This is particularly timely considering several upcoming active remote sensing datasets that will be used to map forest biomass at a global scale (e.g. NASA's GEDI, NISAR, ESA's BIOMASS).
