iDiscovery

iDiscovery puts rigorous state-of-the-art machine learning algorithms, interactive data analysis tools, and intuitive visualization windows into the hands of researchers. Following the analysis, it can display the results in either a single study view (SSV) or in a single patient view (SPV). The multi-layer analysis solution allows a much cleaner interpretation of high-throughput experimental omics data in translational research.

Study managers, clinical operation managers, clinical research coordinators, scientists, clinical investigators, clinicians, oncologists, cancer researchers, principal investigators, lab personnel, bioinformaticians

• Translational researchers frequently struggle with cross-functional visualization of multi-dimensional clinical and omics data. Clinical researchers need to understand these associations in order to discover safer and more effective drugs. There is an unmet need to developing such tools in order to speed up correlative studies that aid in mining novel patient-centric insights.
• The lack of validated biomarkers for guiding clinical trials is one of the most common problems that lead to many failures. Patient-level “omics” differences in individual tumors can lead to dramatic differences in response rate even within patients that present similarly aggressive cancer (stage, grade, the degree of metastasis etc.).
• Many targeted immunotherapies are directed against a single agent or molecule. However, therapies directed against a singular molecular target can often fall short as compensatory mechanisms allow cancer cells to thrive. Such compensatory mechanisms, in many cases, reflect “omic”-level differences in susceptible individuals. Such differences can be unearthed only through pharmacogenomic studies that allow “patient-level cancer vulnerabilities” to stand out.

• iDiscovery puts rigorous state-of-the-art machine learning algorithms, interactive data analysis tools, and intuitive visualization windows into the hands of researchers.
• The flexible client-server architecture employs a rich set of application programming interfaces (APIs) enabling superimposition of omics data on clinical data.
• Since the platform is centrally managed, secure, and scalable – it can be easily integrated into pre-existing bioinformatics environments.
• The powerful application hosts interactive data exploration and visualizations tools that are complemented with an impressive range of statistical applications (data normalization, Principal Components Analysis (PCA), heat-maps, volcano plots, boxplot, t-Test, ANOVA, predictive models etc.).