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This article describes a classifier to identify drug-binding cavities on the basis of physicochemical, structural and geometric attributes of proteins.
The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of protein subcellular localization, and it can be used to predict the other attributes of proteins.
The COS-7 cell line has been used for in vitro expression to reveal the fundamental attributes of proteins, including those proteins that are required for hearing [17], [42].
The attributes of proteins that exhibit unusually slow unfolding have not been elucidated.
However, the attributes of proteins that exhibit unusually slow unfolding are unclear.
The AAC-discrete model was widely used for identifying various attributes of proteins (see, e.g., [ 57– 61]).
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Prior to prediction of protein interactions, we represent every protein pair in our datasets using binary feature sets corresponding to attributes of protein pairs.
We then pursued any recurring patterns of the functional attributes of protein interactions.
The GO slim attributes of protein vertices and type attributes of PPI data are given in Figure 3b.
Since we focused on the recurring patterns of the functional attributes of protein interactions, we used the molecular function ontology.
Of course, as discussed in the introduction, selection may also be acting on attributes of protein sequence that are independent of these types of function.
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