Causaly, a London- and Athens-based AI for cause and effect discovery announces the completion of a $1 million seed investment. Causaly's machine reading platform analyses millions of documents to create unique knowledge graphs highlighting cause and effect relationships in biomedicine. These graphs have the opportunity to visualize connections between millions of variables and factors for the first time.
Understanding the causal basis behind biomedical mechanisms is key when it comes to disease diagnosis and treatment. Causaly's innovation comes at a time when researchers from industry and academia must cope with an exponentially increasing corpus of scientific literature, within which these relationships are recorded. PubMed, the world's leading search engine for biomedical literature includes over 28 million entries, and currently adds more than 1,000,000 citations per year. Comprehensively assessing this body of work is both time consuming and inefficient, a key challenge when it comes to developing treatments or diagnosing patient conditions. Causaly's natural language processing AI connects the dots automatically. The program's semantic search function and knowledge graphs allow scientists and practitioners to easily explore causal associations and instantly gather evidence, a process that in the past could take weeks or months to perform.
Yiannis Kiachopoulos, Causaly's co-founder & CEO, stated: "Getting to the question of how and why things work is at the core of making hypotheses and decisions. We aim to give this access to people who solve very complex problems in Drug Discovery, Health Economics and Pharmacovigilance for developing better and safer treatments".
Kiachopoulos and co-founder Artur Saudabayev aim to build a causal model of the entirety of world's research. The cofounders met while taking part in Singularity University's Global Solutions Program. The selective program encourages innovators to build "moonshots", innovations designed to address global challenges with bold thinking and radical solutions. It is from this perspective that led the founders to create Causaly.
Kiachopoulos explains, "My co-founder Artur had recommended a great book to me; “Guns, Germs and Steel” by Jared Diamond. This book is full with reasons for how civilizations develop based on a variety of resources and conditions present in their environments. As I was reading the book and taking notes on how this system of relationships works, 450 pages later I was left frustrated that I had only captured a fraction of the system knowledge of the book. Interestingly, Artur had faced exactly the same issue and so we decided to see whether we can develop algorithms which transform text into causal system diagrams - and Causaly was born. It was only later that we chose the biomedical domain as the first area to start applying our technology because we felt we could contribute most in this research-intensive domain with significant impact on human health."
Causaly's machine reading platform has broad applications in biomedicine and beyond. Today, Causaly has extracted more than 100 million causal associations from published academic literature, and is working with clients including Novartis, the Swiss multinational pharmaceutical company.
Causaly intends to use this investment round to continue to scale their team and accelerate product development. The seed investment was led by Athens-based Marathon Venture Capital, alongside angel investors including Matt Clifford, Nadav Rosenberg, Charlie Songhurst, Dr. Alexander Moscho and Emerge Education. Marathon's investment in Causaly follows recent investments in agricultural hardware company Augmenta and cloud accelerator firm InAccel.