Former ARMS researcher Artur Saudabayev has co-founded a biomedical AI startup Causaly, which recently announced closing a $1 million seed investment round.

Artur and his co-founder Yiannis Kiachopolous developed a Machine reading platform, which processes millions of scientific papers and extracts cause and effect relationships from them. Subsequently, building Cause and Effect knowledge graphs, Causaly enables users to access semantic search and advanced analytics features on a large knowledge base (currently more than 100 million of relationships over millions of academic papers).

Users for example can switch from keyword search approach to semantic search, for example, asking questions like “How is obesity related to the risk of cancer?” or “What are the effects of caffeine on human health in context of Multiple Sclerosis?” This approach offers increased efficiency, promising to substantially cut human reading effort and widen the scope of covered papers. In addition, the platform quickly provides data on advanced problems, for example disease common cause and comorbidity analysis.

The round was led by Marathon Venture Capital alongside angel investors including Matt Clifford, Nadav Rosenberg, Charlie Songhurst, Dr. Alexander Moscho and Emerge Education.