Today, the amount of data available to researchers is vast. Robust patient registries and collections of data allow for new insights that can help speed the discovery of biomarkers and disease mechanisms that inform new treatment options. In order for data to yield evidence, it needs to transform into mechanistic knowledge (e.g. knowledge of cause and effect relationships), be validated, understood and shared—at scale and without bias. At GNS Healthcare, we’re helping foundations, academic institutions, and researchers create in silico disease models from emerging and established data sources to uncover new disease pathways, discover novel drug targets and drugs, and reveal novel biomarkers and create corresponding diagnostics. GNS creates data-driven computer models that enhance research initiatives, advance treatment innovation, and lead to the prevention and cure of diseases.
Understand Disease Networks
Understand disease pathways, identify disease drivers, and improve treatment and care.
Identify Better Interventions
Identify novel interventions to address unmet patient need.
Share in silico models to accelerate research and collaboration across the research continuum.
“Our knowledge of cause-and-effect relationships and fundamental aspects of biological systems has grown substantially since 2013, when we first licensed the GNS REFS causal machine learning platform.”
Principal Investigator and Senior Research Scientist
Harvard Medical School Department of Systems Biology
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