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Compared with most existing protein structure prediction systems, our approach contains a unique and novel model combination step that can refine protein models by averaging complementary good models or fragments.
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For the model combination and refinement step of our pipeline, we used a novel global local model combination algorithm.
In the combination step, the posterior distributions of model parameters and discrepancy corresponding to different priors are combined into a single distribution based on the probabilistic weights derived from the validation step.
Language Model Combination and Adaptation Using Weighted Finite State Transducers.
(2) Combination step.
(Phase combination step, PHSCMB).
parallel model combination.
Then, we reformulate the combination step.
The algorithm consists of two steps: an adaptation (processing) step followed by a consultation (combination) step.
Our multi-level combination pipeline (Fig. 1) for protein structure prediction is generally comprised of five steps: (i) template identification and ranking, (ii) multi-template combination, (iii) model generation, (iv) model evaluation and (v) model combination and refinement.
We examined allelic allelic, dominant dominant, dominant recessive, recessive dominant and recessive recessive model combinations, depending on the significant mode found on the univariate step.
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