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the metalife predictor is based on the up-to-date methods of top development and technologies for these purposes. various algorithms are used for improvement and mutual combinations in order to maximize the correctness of forecasts. discrete intrinsic molecular characteristics are used by these algorithms. the metalife predictor contains also internal "machine learning" algorithms for the formation of functional characteristics of biological objects. one of functions realized by metalife predictor is the use of sequence-based vector analyses in multidimensional frames. these self-learning algorithms are useful for the forecasts of protein-protein interactions, subcellular localization of either prokaryontic and also eukaryontic proteins as well as in ontology-based classification of proteins.
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