Predicting the decision making chemicals used for bacterial growth.

ABSTRACT: Predicting the contribution of media components to bacterial growth was first initiated by introducing machine learning to high-throughput growth assays. A total of 1336 temporal growth records corresponding to 225 different media, which were composed of 13 chemical components, were generated. The growth rate and saturated density of each growth curve were automatically calculated with the newly developed data processing program. To identify the decision making factors related to growth among the 13 chemicals, big datasets linking the growth parameters to the chemical combinations were subjected to decision tree learning. The results showed that the only carbon source, glucose, determined bacterial growth, but it was not the first priority. Instead, the top decision making chemicals in rel

SUBMITTER: Ashino K 

PROVIDER: S-EPMC6510730 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature