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Prediction of protein solubility in Escherichia coli using logistic regression
Author(s) -
Diaz Armando A.,
Tomba Emanuele,
Lennarson Reese,
Richard Rex,
Bagajewicz Miguel J.,
Harrison Roger G.
Publication year - 2009
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.22537
Subject(s) - logistic regression , solubility , stepwise regression , predictive modelling , escherichia coli , regression , regression analysis , linear discriminant analysis , computer science , chromatography , biological system , chemistry , statistics , mathematics , artificial intelligence , machine learning , biology , biochemistry , organic chemistry , gene
In this article we present a new and more accurate model for the prediction of the solubility of proteins overexpressed in the bacterium Escherichia coli . The model uses the statistical technique of logistic regression. To build this model, 32 parameters that could potentially correlate well with solubility were used. In addition, the protein database was expanded compared to those used previously. We tested several different implementations of logistic regression with varied results. The best implementation, which is the one we report, exhibits excellent overall prediction accuracies: 94% for the model and 87% by cross‐validation. For comparison, we also tested discriminant analysis using the same parameters, and we obtained a less accurate prediction (69% cross‐validation accuracy for the stepwise forward plus interactions model). Biotechnol. Bioeng. 2010; 105: 374–383. © 2009 Wiley Periodicals, Inc.