A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action

@article{Yang2019AWM,
  title={A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action},
  author={Jason H. Yang and Sarah N. Wright and Meagan Hamblin and Douglas McCloskey and Miguel A. Cadena Alcantar and Lars Schr{\"u}bbers and Allison J. Lopatkin and Sangeeta Satish and Amir Nili and Bernhard O. Palsson and Graham C. Walker and James J. Collins},
  journal={Cell},
  year={2019},
  volume={177},
  pages={1649-1661.e9}
}

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References

SHOWING 1-10 OF 116 REFERENCES

Biological Machine Learning Combined with Campylobacter Population Genomics Reveals Virulence Gene Allelic Variants Cause Disease

The capability of machine learning coupled with GWAS and population genomics to simultaneously identify and rank alleles to define their role in infectious disease mechanisms is defined.

Using deep learning to model the hierarchical structure and function of a cell

DCell, a VNN embedded in the hierarchical structure of 2,526 subsystems comprising a eukaryotic cell, provides a foundation for decoding the genetics of disease, drug resistance and synthetic life.

Next-Generation Machine Learning for Biological Networks

How antibiotics kill bacteria: from targets to networks

The multilayered effects of drug–target interactions, including the essential cellular processes that are inhibited by bactericidal antibiotics and the associated cellular response mechanisms that contribute to killing are discussed.

Antibiotic efficacy-context matters.

Recon3D: A Resource Enabling A Three-Dimensional View of Gene Variation in Human Metabolism

Recon3D is presented, a computational resource that includes three-dimensional metabolite and protein structure data and enables integrated analyses of metabolic functions in humans and is used to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs.

Antibiotics induce redox-related physiological alterations as part of their lethality.

This work provides direct evidence that, downstream of their target-specific interactions, bactericidal antibiotics induce complex redox alterations that contribute to cellular damage and death, thus supporting an evolving, expanded model of antibiotic lethality.
...