Towards personalised medicine: One type of data is not enough
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IMAGE: Artist’s interpretation of multiple molecular layer analysis. view more 

Credit: EMBL-EBI

19 June, Heidelberg, Hinxton – EMBL researchers have designed a computational method to jointly analyse multiple types of molecular data from patients in order to identify molecular signatures that distinguish individuals. The method is called Multi-Omics Factor Analysis (MOFA), and was published in Molecular Systems Biology today. MOFA could be particularly useful for understanding cancer development, improving diagnosis and suggesting new directions for personalised treatment.

The researchers tested their new method on multi-omics data collected from 200 leukaemia patients. MOFA identified a series of factors that highlight the molecular variability between patients. This information could help researchers understand how cancer develops at an individual level. It could also help steer personalised treatment decisions.

“The big challenge in cancer is that each patient’s disease is different from a molecular point of view and has a unique set of molecular features that have led to its development,” explains Ricard Argelaguet, Predoctoral Fellow in the Stegle group at the European Bioinformatics Institute (EMBL-EBI). “Our method allows researchers to do something that couldn’t be done before – to easily integrate complex molecular data from DNA, RNA, methylation and more to build

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