Improving Protein Expression Prediction Using Extra Features and Ensemble Averaging.
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ABSTRACT:
SUBMITTER: Fernandes A
PROVIDER: S-EPMC4775025 | biostudies-literature | 2016
REPOSITORIES: biostudies-literature
Publications
Improving Protein Expression Prediction Using Extra Features and Ensemble Averaging.
Fernandes Armando A Vinga Susana S
PloS one 20160302 3
The article focus is the improvement of machine learning models capable of predicting protein expression levels based on their codon encoding. Support vector regression (SVR) and partial least squares (PLS) were used to create the models. SVR yields predictions that surpass those of PLS. It is shown that it is possible to improve the models predictive ability by using two more input features, codon identification number and codon count, besides the already used codon bias and minimum free energy ...[more]
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