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GENSCAN gene model prediction algorithm was used to predict introns and exons.
A GENSCAN gene model prediction algorithm [ 60] was used to predict introns and exons, and the resulting predictions were searched against the Uniref50 (clustered sets of sequences from UniProt Knowledgebase) database [ 61].
The repeat masked contig sequences were searched against NCBI nr database using both BLASTX searches and GENSCAN gene model prediction algorithm [ 36] to identify the genes in the assembled region.
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In most cases these regional differences were not supported by EST evidence and likely represent artifacts resulting from automated gene model prediction algorithms.
Mathematical models, prediction algorithms and visualization tools (see for example Martino's work [107]) have recently shed light on this routine, allowing to construct better human displacement models which can be used to predict epidemics outbreaks.
We therefore included these variables in the final model and risk prediction algorithm.
Searches of the algal genomes were carried out using the Department of Energy (DoE) Joint Genome Institute (JGI) database (http://genome.jgi.doe.gov) using the latest releases as of April 2014 of the dataset created with 'all models' gene prediction algorithms.
On the basis of this model, we propose a prediction algorithm for disease dynamics.
The path loss prediction models incorporated in the prediction algorithm are based upon, and validated with path loss measurements in different buildings.
Overall, we conclude that, for the paradigmatic model, both previous prediction algorithms and BP are relatively successful in yielding accurate positional probabilities.
In this paper, we build on this scheme by leveraging the grey model first-order one variable (GM 1,1)) prediction algorithm, first proposed in [8] by treating these network scores, which represent the overall QoS of the network, as input into the prediction model.
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