Our role is to develop or assist in the development of predictive tools that will make use of clinical observations and chemogenomic measures of patient samples to provide guidance to clinicians in selecting treatment. For this, we favor a two-pronged approach.
First, we emphasize the role of presentation tools to directly empower clinician-researchers to mine the data in the most transparent way possible.
In parallel, we apply modern machine learning techniques to derive clinically useful prediction models and thus contribute to the development of a more formal model of data integration.
As a foundation to the development of predictive tools, we have also been constantly refining an RNA-Seq analysis pipeline, providing information regarding gene expression levels and sequence variants (SNPs, indels and fusions) but also reporting on novel genes and quantifying splice variants.