Imperial College London

MissSilviaColicino

Faculty of MedicineNational Heart & Lung Institute

 
 
 
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Contact

 

s.colicino

 
 
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Location

 

Emmanuel Kaye BuildingRoyal Brompton Campus

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Summary

 

Summary

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.

Publications

Journals

Munblit D, Treneva M, Peroni DG, 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

Guo L, Li P-H, Li H, 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

Conference

Koroleva D, Beznosova L, Lototskaya P, 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

Nakamura T, Haider S, Colicino S, 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

Colicino S, Minelli C, Lewin A, 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

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