Bioprocess data mining using regularized regression and random forests.

ABSTRACT:

Background

In bioprocess development, the needs of data analysis include (1) getting overview to existing data sets, (2) identifying primary control parameters, (3) determining a useful control direction, and (4) planning future experiments. In particular, the integration of multiple data sets causes that these needs cannot be properly addressed by regression models that assume linear input-output relationship or unimodality of the response function. Regularized regression and random forests, on the other hand, have several properties that may appear important in this context. They are capable, e.g., in handling small number of samples with respect to the number of variables, feature selection, and the visualization of response surfaces in order to present the prediction results in

SUBMITTER: Hassan S 

PROVIDER: S-EPMC3750505 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature