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Shiga-Toxigenic Escherichia coli

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Machine learning models for prediction of Escherichia coli O157:H7 growth in raw ground beef at different storage temperatures.

Meat science
Shiga toxin-producing Escherichia coli (STEC) can be life-threatening and lead to major outbreaks. The prevention of STEC-related infections can be provided by control measures at all stages of the food chain. The growth performance of E. coli O157:H...

Identifying non-O157 Shiga toxin-producing Escherichia coli (STEC) using deep learning methods with hyperspectral microscope images.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups such as O26, O45, O103, O111, O121 and O145 often cause illness to people in the United States and the conventional identification of these "Big-Six" are complex. The label-free hypers...

Predicting Host Association for Shiga Toxin-Producing E. coli Serogroups by Machine Learning.

Methods in molecular biology (Clifton, N.J.)
Escherichia coli is a species of bacteria that can be present in a wide variety of mammalian hosts and potentially soil environments. E. coli has an open genome and can show considerable diversity in gene content between isolates. It is a reasonable ...

Pathogenic potential assessment of the Shiga toxin-producing by a source attribution-considered machine learning model.

Proceedings of the National Academy of Sciences of the United States of America
Instead of conventional serotyping and virulence gene combination methods, methods have been developed to evaluate the pathogenic potential of newly emerging pathogens. Among them, the machine learning (ML)-based method using whole-genome sequencing ...