NEW YORK (PRWEB) OCTOBER 31, 2019
drug360 graph, a breakthrough knowledge graph product, has just been released by pharma artificial intelligence pioneer tellic. drug360 brings tellic’s expertise in biomedical language processing and machine learning to bear on biomedical big data. This new tool allows researchers to quickly uncover relationships between genes, diseases, variants, phenotypes, and other biomedical entities. Researchers and analysts can use drug360 graph to dramatically increase their efficiency searching multiple data sources and uncover valuable genetic knowledge supporting decisions around their pipelines.
“drug360 graph provides greater confidence around the crucial decisions researchers make regarding advancing drug candidates or abandoning them,” says Richard Wendell, tellic CEO. “Research shows that genetic evidence can double the chances of a clinical trial’s success.”
drug360 graph contains proprietary data on 180 million relationships between diseases, genes, gene variants, phenotypes, and other biomedical entities. This data is generated by tellic’s patent-pending deep learning algorithms, which scan biomedical text to extract entities, normalize them to the desired concept within biomedical ontologies, and quantify the strength of relationships. Quantifying the strength of these relationships is crucial, because it helps remove the “noise” from the signal. “tellic’s proprietary deep learning technologies incorporate biomedical context which results in high quality genetic evidence data that can improve clinical trial success,” says Wendell.
With drug360 graph, researchers can:
- Immediately be up and running with a biomedical knowledge graph pre-populated with 180 million genetic relationships
- Easily import their internal data into tellic’s proprietary data to create a Knowledge Graph that is unique to their organization
- Quickly identify unexpected genetic linkages to inform target selection, clinical trial design, BD and biotech investment decisions.
- Drill down and search genetic relationships of interest to pinpoint each sentence in the source data that was used to generate the relationship
- Export search results into a CSV or Excel for further analysis
drug360 currently contains millions of additional relationships between genes or gene variants than those found in public databases. Available as software as a service, the product is easy to use and is well supported.
Big data, machine learning, and natural language processing are brought together by tellic into drug360 to form a single, unparalleled source of clear-cut insights about associations between genes and diseases. The company was founded in 2015 by Richard Wendell, a Fortune 500 Chief Data Officer, whose vision was to make it easy for pharma companies to apply emerging data science technologies to their pipelines. tellic pioneers a new category of enterprise-scale biomedical language processing and knowledge discovery powered by machine learning. For more information or to arrange a demo of drug360 graph, please contact [email protected]
drug360 graph provides greater confidence around the crucial decisions researchers make regarding advancing drug candidates or abandoning them, says Richard Wendell, tellic CEO. Research shows that genetic evidence can double the chances of a clinical trial’s success.