AIMC Topic: Legionella pneumophila

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Backtracking metabolic dynamics in single cells predicts bacterial replication in human macrophages.

Nature communications
Accurately tracking dynamic state transitions is crucial for modeling and predicting biological outcomes, as it captures heterogeneity of cellular responses. To build a model to predict bacterial infection in single cells, we have monitored in parall...

Development of a machine learning model to support low cost real-time Legionella monitoring in premise plumbing systems.

Water research
Legionella pneumophila (L. pneumophila) is a pathogenic bacterium primarily known for causing Legionnaires' Disease which is known for high mortality rates, particularly in the elderly. With caseloads continuing to increase, further research is neede...

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

PloS one
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 ma...