Kristina M Hette


Position: Former Member Hette, Kristina M
Research areas:
  • Uncategorized
JRESEARCH_PHONE: +31 715269454

Dr. Kristina Hettne was born in Råda, Sweden, on March 2, 1978. She followed The Health Care Programme at the Swedish Upper Secondary School and graduated in 1997, after which she embarked on an around-the-world trip. Coming back to Sweden in 1998, she followed courses in natural science subjects provided by the Municipal Adult Education in Sweden, preparing her for a university study combining biology and computer science. In 1999 she started her study in Bioinformatics at the University of Skövde, Sweden. In 2003 she obtained her M.Sc. degree in Computer Science, focusing on Bioinformatics with the thesis “Using nuclear receptor interactions as biomarkers for metabolic syndrome”. The work was carried out at the Bioinformatics department at AstraZeneca R&D, Mölndal, Sweden. In 2004 she started working as a Scientist at the Safety Assessment department at AstraZeneca R&D Mölndal, Sweden. During her time at AstraZeneca Kristina was involved in research projects aimed at unraveling parts of the biology behind two diseases, the Complex Regional Pain Syndrome I (CRPS-I) and periodontitis, and in research surrounding nuclear receptor pathways and their relevance for the safety of drugs. In August 2006 she started as a PhD student in the Biosemantics group at the Erasmus MC, Rotterdam, on a project to develop class prediction tools for toxicological classification, and to identify molecular and genetic pathways linking expression profiles and specific toxic phenotypes. The project was a collaboration between the department of Toxicogenomics at Maastricht University and the department of Medical Informatics at Erasmus MC. In June 2011 she joined the Biosemantics group at the Human Genetics department at the Leiden University Medical Center as a postdoctoral researcher, combining her interest in health care, biology, and computer science.

PhD thesis: Next-generation text-mining applied to toxicogenomics data analysis