Table of Contents

November 2017

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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Initial-state-dependent, robust, transient neural dynamics encode conscious visual perception

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Daniel Monsivais, Asim Ghosh, Kunal Bhattacharya, Robin I. M. Dunbar, Kimmo Kaski

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Thomas Coudrat, John Simms, Arthur Christopoulos, Denise Wootten, Patrick M. Sexton

Significant associations between driver gene mutations and DNA methylation alterations across many cancer types

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Richard D. Smith, Jing Lu, Heather A. Carlson

A modeling study of budding yeast colony formation and its relationship to budding pattern and aging

Yanli Wang, Wing-Cheong Lo, Ching-Shan Chou

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Reading wild minds: A computational assay of Theory of Mind sophistication across seven primate species

Marie Devaine, Aurore San-Galli, Cinzia Trapanese, Giulia Bardino, Christelle Hano, Michel Saint Jalme, Sebastien Bouret, Shelly Masi, Jean Daunizeau

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