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had dominant influence on PMB and the environmental temperature index was a better predictor for predicting PMB; AMB was influenced by both landscape and temperature variables and the proposed integrated habitat index was better for prediction of AMB in Poyang Lake wetland.
The present study was devoted to develop a new and more powerful predictor for predicting eukaryotic protein subcellular localization by addressing the above three problems.
The present study was initiated in an attempt to develop a new and more powerful predictor for predicting plant protein subcellular localization by addressing the above four problems.
Table 1 shows the overall performance of the SVM predictor for predicting sequence-specific and non-specific binding residues in the first stage.
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Finally, four predictors for predicting the interactions of drugs with each of the four protein families are developed in hopes that they can help provide useful information for drug design.
We explored the performance of RF-based predictors for predicting RNA-binding proteins by various features.
The gene expression data served as predictors for predicting a dummy-coded response vector.
To further understand the accurate levels of the predictors for predicting ARDS, we used stratified analysis for each index.
Therefore, the cost-sensitive ensemble algorithm that incorporates cost-sensitive scheme produces more robust performance than conventional ensemble simply combining multiple predictors for predicting.
The procedure finds an optimally weighted sum of predictor variables for predicting the dependent variable.
Therefore, the prediction of non-specific binding residues can increase the predictor's capability for predicting DNA-binding residues.
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