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We perform integrated multi-omics analysis, using our proprietary technology platforms. In doing so, we exploit our internal databases with millions of annotated pharmacology and toxicology relevant relationships between small molecules, drug targets, biological pathways, pathologies & diseases. Our analysis pipeline allows for rapid interrogation of genomics, proteomics, chemical and clinical data to allow us to identify mechanistic insights, clinically relevant pathways and potential therapeutic strategies.

Raw data analysis – we use computational and statistical approaches to process raw genomics and transcriptomics data, in order to produce a ranked list of genes that are found to be of significant importance in the experiment and further used as input to the data integration, forming a basis for the interpretation of the data.

Post factor analysis – by applying different factor analysis algorithms, we discover variable changes in multi-omics datasets to reveal potentially functional change with biological relevance.

Clinical clustering – we apply computational and statistical approaches to perform cluster analysis of clinical variables/data, in order to define clinical subgroups that can facilitate patient management.