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个人简介
Raquel’s academic background is in mathematics and statistics. Her interest is applying statistical and machine learning methods to identify clinical and genetic predictors of risk to complex disorders and response to treatment. Her work includes the development of novel models to predict the response to antidepressant treatments at the individual level, and to estimate the effect of genetic ancestry measures in the response to treatments. Raquel is an active and dedicated lecturer, and teaches regularly in introductory and advanced courses on statistics and machine learning for MSc and PhD students, in UK and abroad. She organises very popular monthly seminars on Machine Learning and the annual workshop in Machine Learning for health Informatics and Bioinformatics at King’s. This has made her an effective advocate for machine learning and big data methods in psychiatry.
Research Interests:
Computational Statistics
Machine Learning
Bioinformatics
Precision Medicine
Genetic Epidemiology
Topological Data Analysis
Research Interests:
Computational Statistics
Machine Learning
Bioinformatics
Precision Medicine
Genetic Epidemiology
Topological Data Analysis
研究兴趣
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HUMAN HEREDITYno. SUPPL 1 (2023): 9-9
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AI and Ethicspp.1-13, (2023)
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