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PLoS Computational Biology Issue Image | Vol. 13(11) November 2017

PLoS Computational Biology Issue Image | Vol. 13(11) November 2017

PLOS
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Predicting HIV-1 neutralization by antibodies using machine learning

Broadly neutralizing antibodies against HIV-1 are currently investigated in clinical trials as a new treatment option. In order to select an effective antibody therapy, the neutralization sensitivity of the patient's viral strains towards the antibodies must be ensured. Since neutralization assays are too time-consuming and expensive, they are not suitable for routine clinical practice. Applying machine learning on existing neutralization assay data, Hake and Pfeifer developed a model for accurately predicting the neutralization sensitivity of HIV-1 towards antibodies based on the viral envelope sequence. The illustration sketches how the classifier discriminates between susceptible and resistant samples.

Image Credit: Anna Hake

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Predicting HIV-1 neutralization by antibodies using machine learning

Broadly neutralizing antibodies against HIV-1 are currently investigated in clinical trials as a new treatment option. In order to select an effective antibody therapy, the neutralization sensitivity of the patient's viral strains towards the antibodies must be ensured. Since neutralization assays are too time-consuming and expensive, they are not suitable for routine clinical practice. Applying machine learning on existing neutralization assay data, Hake and Pfeifer developed a model for accurately predicting the neutralization sensitivity of HIV-1 towards antibodies based on the viral envelope sequence. The illustration sketches how the classifier discriminates between susceptible and resistant samples.

Image Credit: Anna Hake

https://doi.org/10.1371/image.pcbi.v13.i11.g001