SOMA: AI Research Platform for Medical Causal Discovery

SOMA transforms how medical researchers approach literature review and hypothesis generation by automating the discovery of undocumented pathways between factors and health outcomes. Rather than manually sifting through thousands of studies, researchers can rely on SOMA's AI-driven knowledge graph, built from a vast database of open access journals, to surface causal and associative relationships that might otherwise take months to uncover. At its core, SOMA analyzes medical research articles, extracts key concepts, and maps the relationships between them into a specialized, searchable database. Users simply input a pair of concepts—such as a compound and a disease—and SOMA returns documented causal chains, with every link backed by a sentence pulled directly from peer-reviewed literature. This evidence-based approach means researchers aren't just getting AI-generated guesses; they're getting traceable, citable connections rooted in published science. By claiming speedups of up to 100x over traditional literature review methods, SOMA is positioned as a force multiplier for scientific discovery, allowing researchers, academics, and healthcare professionals to spend less time organizing information and more time advancing actual research.

1. Enter a pair of concepts representing a potential cause and effect (e.g., a factor and a medical condition). 2. Set a filter to define the desired length of causal chains you want to explore. 3. Review the causal chains returned by SOMA, each link supported by a sentence extracted from a real research article. 4. Click through the provided web links to access the original source articles for deeper verification and citation.