SOMA is a research automation platform designed to accelerate medical innovation. It analyzes publicly available medical research articles and extracts important concepts, as well as identifies causal and associative relationships between them.
The extracted information is organized into a specialized database known as a knowledge graph. Researchers can input specific pairs of concepts, and the system retrieves relevant causal chains with customizable lengths.The platform also enhances literature reviews by finding articles relevant to the researcher’s specified cause and effect relationships.
It captures documents based on the mechanism of action, allowing users to uncover hidden connections. This feature enables researchers to spend less time organizing reviews and focus more on their own research.SOMA aims to save users up to 80% of the time typically spent on document pre-processing.
More details about SOMA
Can SOMA help me uncover hidden connections in medical research?
Yes, SOMA can help you uncover hidden connections in medical research. It does this by finding articles not only based on your specified keywords or concepts but also related concepts that may not have been mentioned in a single document.
What is the customizable length of causal chains in SOMA?
The length of causal chains in SOMA is customizable by the user. You can set the filter to specify the length of the causal chains you want the system to retrieve.
Can SOMA help me to spend less time on document pre-processing and focus more on my own research?
Yes, SOMA can help you focus more on your own research as it can save you up to 80% of the time typically spent on document pre-processing. It accelerates the analysis of published research, thus enabling you to build on existing knowledge and driving your research forwards.
How can I obtain access to specific research articles in SOMA?
On SOMA, specific research articles can be accessed via the web links provided by the system. Each link in every causal chain is documented by a sentence extracted from a research article which you can access directly.
