Silvia Colicino is a Biostatistician at Imperial College London; her focus is the application of Bayesian Analysis and Machine Learning to investigate causes of asthma phenotypes and their association to environmental factors.
Her role is to develop novel statistical tools that can be used to better understand the mechanisms of respiratory disease, and to predict the natural progression and potential remission of the condition.
Silvia holds a MSc in Biostatistics and Experimental Statistics from the University of Milano-Bicocca, Italy, a BSc in Statistics and Data Management and has been a Research Assistant at Harvard School of Public Health, Boston, Massachusetts.
et al., 2017, Immune components in human milk are associated with early infant immunological health outcomes: a prospective 3 country analysis, Nutrients, Vol:9, ISSN:2072-6643
et al., 2016, Effects of environmental noise exposure on DNA methylation in the brain and metabolic health, Environmental Research, Vol:153, ISSN:0013-9351, Pages:73-82
et al., 2018, Asthma predictive index external validation in Russian population, Congress of the European-Academy-of-Allergy-and-Clinical-Immunology (EAACI), WILEY, Pages:533-534, ISSN:0105-4538
et al., 2018, Defining childhood eczema: The variety of operational definition causes divergence in its prevalence estimate and model performance, Congress of the European-Academy-of-Allergy-and-Clinical-Immunology (EAACI), WILEY, Pages:108-109, ISSN:0105-4538
et al., 2017, PREDICTING ASTHMA IN LATER CHILDHOOD: A GENERAL AND HIGH-RISK POPULATION APPROACH, Winter Meeting of the British-Thoracic-Society, BMJ PUBLISHING GROUP, Pages:A38-A38, ISSN:0040-6376