Artificial IntelligenceEuretos provides free access to its AI Platform for academic researchers to further support early disease and drug research

Euretos is pleased to announce that access to its AI Platform will be available for free to all academic users. The integrated data, toolset and workflows that have long been used by the corporate research community in drug development are now made available to the academic research community.

The Euretos AI Platform is currently being used with some of the largest pharma and biotech companies in the world as well as in a number of large public-private partnerships,” says Aram Krol, CEO of Euretos. “By making our platform available for free for academic researchers, who are mainly involved in fundamental early disease research, we want to make our contribution to supporting this crucial early investigative effort.

The Euretos Platform offers access to the world’s largest machine-read knowledge base to provide academic researchers with intelligent and proactive search capabilities, powerful and intuitive analytics functions, visualised relation maps, and many specific workflows for data-driven disease insights such as for target assessment. In addition, it contains curated public data, such as a comprehensive cell type expression library.

Access to the platform can be requested here: View plans and pricing

Biological research technologies have advanced significantly over the last two decades, providing a wealth of new data covering increasing molecular diversity and more significant manipulation and understanding of biological systems. This has lead to an explosion in the publication of scientific papers and research data. Until now, few tools were available to navigate this wealth of information and to assess the relevance for each biologist’s research projects. The Euretos AI Platform now provides an integrated view of publications and relevant research data in combination with user-friendly features and workflows to enhance fundamental disease and drug research productivity.

For example, users can create gene sets from literature automatically and overlay them with the results of their wet-lab experiments and understand how these are enriched in the various relevant cell types. From there, biologists can infer the molecular pathways explaining their experimental results, all without depending on bioinformatics experts.


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