Using an optimal set of features with a machine learning-based approach to predict effector proteins for Legionella pneumophila.

ABSTRACT: Type IV secretion systems exist in a number of bacterial pathogens and are used to secrete effector proteins directly into host cells in order to change their environment making the environment hospitable for the bacteria. In recent years, several machine learning algorithms have been developed to predict effector proteins, potentially facilitating experimental verification. However, inconsistencies exist between their results. Previously we analysed the disparate sets of predictive features used in these algorithms to determine an optimal set of 370 features for effector prediction. This study focuses on the best way to use these optimal features by designing three machine learning classifiers, comparing our results with those of others, and obtaining de novo results. We chose the pathoge

SUBMITTER: Esna Ashari Z 

PROVIDER: S-EPMC6347213 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature