Barbara Bodinier
As seen in:
Nature,
PLOS Medicine,
ACS Publications,
Medscape,
Science Magazine,
EClinicalMedicine,
The Lancet
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Predicting gene essentiality and drug response from preclinical perturbation screens with layered ensemble of autoencoders and predictors
Abstract High-throughput preclinical perturbation screens, where the effects of genetic, chemical, or environmental perturbations are systematically tested on disease models, hold significant promise for machine learning-enhanced drug discovery due to their scale and causal nature. Predictive models trained on such datasets can be used to (i) infer perturbation response for previously untested disease models, and (ii) characterise the biological context that affects perturbation response.
Computer Science > Machine Learning
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Variant-specific symptoms of COVID-19 in a study of 1,542,510 adults in England - Nature Communications
Abstract Infection with SARS-CoV-2 virus is associated with a wide range of symptoms. The REal-time Assessment of Community Transmission −1 (REACT-1) study monitored the spread and clinical manifestation of SARS-CoV-2 among random samples of the population in England from 1 May 2020 to 31 March 2022.
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