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In contrast with standard phylogenetic methods, our models include a central hypothesis or axiom that invokes an evolutionary search of conformational order in molecules which defines the general direction of the evolutionary path [22], [23], [25] [28].
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Unlike the TV-regularized methods, our model has the advantage that it performs well for very sparse data sets.
Different from other methods, our model can express the relationship between the node influence and data transmission and depict the dynamic change of node importance by our model in wireless sensor network.
Compared with GeneMarkS, Metagene, Orphelia, and Heuristic Approach methods, our model achieved better prediction performance in short gene classification.
In comparison with GeneMarkS, Metagene, Orphelia, and Heuristic Approachs methods, our model achieved the best prediction performance in identification of short prokaryotic genes.
To overcome the limitation of conventional domain-prediction methods, our model framework regarded dc-pairs as the basic units of PPIs.
Using this method, our models were fitted reasonably well to the various experimental measurements.
Using either method, our models employ consumption of food as a proxy for consumption of PBDEs in food, another source of exposure misclassification.
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