We developed machine learning models, PRIDICT2.0 and ePRIDICT, to predict prime editing efficiency, offering a robust tool for optimizing genome editing strategies across diverse chromatin contexts.
PRIDICT is a machine learning model that accurately predicts prime editing efficiency, validated across diverse genetic edits and various experimental conditions.
Viral testing for pediatric inpatients with respiratory symptoms is common, with considerable associated charges. In an attempt to reduce testing volumes, we studied whether data available at the time of admission could aid in identifying children …
We are looking for a PhD student for our SNF project ”Medical, Multilingual, and Privacy-Preserving Natural Language Processing (M2P2-NLP)” passionate about working on medical problems and can help us create AI tools in the medical field, particularly in oncology and radiology.
🧑🏼🔬 You will be someone who loves to do research, design, and build novel AI models. You are used to working in a research environment. You will have experience in building and evaluating machine learning models and preferably have knowledge and experience in the text and image processing domain.