Work

Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions

Year: 2022

Type: article

Source: Artificial Intelligence in Medicine

DOI: 10.1016/j.artmed.2022.102326

Authors: Mattia Prosperi, Christina Boucher, Jiang Bian, Simone Marini

Institutions: University of Florida, University of Florida Health

Language: English

Topic: Antibiotic Resistance in Bacteria

Subfield: Molecular Medicine

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Science Foundation, National Institutes of Health, National Institute of Allergy and Infectious Diseases

Awards: Forecasting trajectories of HIV transmission networks with a novel phylodynamic and deep learning framework, SCH: INT: Enabling real time surveillance of antimicrobial resistance, G3396921939, Developing Computational Methods for Surveillance of Antimicrobial Resistant Agents

OpenAlex ID: W3181235578

FWCI: 0.491

Cites: 39

Cited by: 11

Related to: 10