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The most significant bottleneck in personalized medicine is the identification of accurate biomarkers for diagnosis, prognosis, targeted therapy and clinical trials. Biology researchers working in academic, BioPharma and applied research laboratories spend 24B USD on this work.

The identification process is slow because biomarkers for most diseases are a heterogeneous combination of genes, proteins, protein complexes, SNPs and/or RNA molecules and the process of their discovery involves complex biological experiments. Determining which biomarkers are relevant from the vast amounts of data generated from these experiments requires sophisticated analytics capability.

Early bioinformatics software was developed to address parts of the biomarker identification process. Often, different software solutions are used to perform different functions in the same research chain, slowing research and leading to inconclusive results.

In contrast, InSyBio has taken an end-to-end approach in applying predictive analytics to biomarker identification: from using proprietary technology in assessing big research data and incorporating public databases to the end result of a refined list of biologically relevant biomarkers.

The InSyBio Suite is a powerful tool for researchers to assess heterogeneous biomarkers. In a study of patients with Parkinson’s, the InSyBio Suite refined the list of biomarkers from over 800 to ~ 50, and achieved more precise predictive results.

Overall, we have demonstrated a reduction in time for biomarker extraction from 6 months to less than one month and in cost by a factor of 8 while also improving the quality of information that can be extracted from biological experiments.

InSyBio’s services are in use by large research organizations, such as the Nestle Institute of Health Sciences, and >200 scientists worldwide and several applications have already been published in biomarker discovery related scientific articles.