MolecularMatch Specializes in Developing Automated Technology That Enhances the Application of Precision Medicine
Others missed 89% of the variants MolecularMatch captured
When comparing MolecularMatch with public databases in an effort to cover a popular lung cancer treatment, MolecularMatch identified substantially more relevant variants. Combined, the four most commonly used molecular databases identified only 11% of the 144 variant annotations that MolecularMatch recommended for this therapy. MolecularMatch increases your productivity and accuracy by removing your dependency on individual institutions and limiting the time and effort needed to assess extensive amounts of data.
Making the power of genetic data more discoverable, understandable, accessible and useful
MolecularMatch takes the complexity of molecular data in any format and turns it into simple, understandable and meaningful information that physicians, pathologists and patients alike can absorb. We are able to deliver a deep understanding of molecular level information at an economic rate that others cannot match.
Our system interprets a vast amount of terminology, formulating associations that are not easily identifiable among such voluminous information. The technology is designed to produce more accurate output by conducting a universal search across disparate data sets, reducing the inconsistencies typically associated with molecularly targeted results.
Improving the utility of genomics with greater data integrity
Our data are validated through a set of predefined test scenarios that ensure our platform remains consistent, reliable and current. Updates to the data are flagged for review and approval by our scientific and medical experts. MolecularMatch is unique in that we are independent of any one academic institution’s philosophy or clinical pathway, allowing us to deliver the most accurate level of evidence for clinical decision making.
Our natural language processing engine extracts a knowledge graph from structured, semi-structured and unstructured data sources. A zoomed-in view of the graph shows dense connections (curved lines) between biological concepts and clinical trials.