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Apart from the two methods discussed in detail here, there are many others for the prediction of functional associations between proteins based on co-evolution and other genomic features, which are termed 'context-based' methods (Valencia and Pazos, 2002; Shoemaker and Panchenko, 2007).
FAST-NMR combines NMR ligand affinity screening [22] using a fragment-based functional library [23] with structural biology and bioinformatics [8] to rapidly determine protein-ligand complexes [24] and infer functional relationship between proteins based on similarities in functional epitopes.
Biozon computes and stores similarity relationships between proteins based on sequence, structure or expression profiles.
CC methods, such as the iterative classification algorithm (ICA), usually explore dependencies between proteins based on the analysis of attributes and functions of neighboring partners.
Rather few studies deal with the signalling flow between proteins based on the analysis of protein activation and abundance coupled with intervention effects.
Traditional UF membranes mostly differentiate between proteins based on their respective sizes, and a tenfold difference is usually necessary for effective separation.
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SDA computes association rates by simulating the diffusion association between two proteins based on Brownian dynamics (see material and methods and [26], [31], [32].
The acceptor photobleaching method is one of the accurate methods available to determine the interaction between two proteins based on the increased intensity of donor fluorescence at the time of acceptor bleaching.
SCAN first defines the structural similarity between two proteins based on their common neighbors.
Thus, we may be inferred functional association by significant domain cooccurrence between two proteins based on InterPro database (10).
We calculated distances between all proteins based on their abundance levels across all observations or their functional similarity based on their annotations in GO.
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