Searching through the mountains of published cancer research could be made easier, thanks to a new AI system developed by scientists.
The system, called LION LBD and developed by researchers at the University of Cambridge in the UK, has been designed to assist scientists in the search for cancer-related discoveries.
It is the first literature-based discovery system aimed at supporting cancer research, according to the study published in the journal Bioinformatics.
Global cancer research attracts massive amounts of funding worldwide, and the scientific literature is now so huge that researchers are struggling to keep up with it: critical hypothesis-generating evidence is now often discovered long after it was published.
Cancer is a complex class of diseases that are not completely understood and are the second-leading cause of death worldwide, researchers said.
Cancer development involves changes in numerous chemical and biochemical molecules, reactions and pathways, and cancer research is being conducted across a wide variety of scientific fields, which have variability in the way that they describe similar concepts.
"As a cancer researcher, even if you knew what you were looking for, there are literally thousands of papers appearing every day," said Professor Anna Korhonen, Co-Director of Cambridge's Language Technology Lab.
"LION LBD uses AI to help scientists keep up-to-date with published discoveries in their field, but could also help them make new discoveries by combining what is already known in the literature by making connections between sources that may appear to be unrelated," Korhonen said.
The 'LBD' in LION LBD stands for Literature-Based Discovery, a concept developed in the 1980s which seeks to make new discoveries by combing pieces of information from disconnected sources.
The key idea behind the original version of LBD is that concepts that are never explicitly linked in the literature may be indirectly linked through intermediate concepts.
The design of the LION LBD system allows real-time search to discover indirect associations between entities in a database of tens of millions of publications while preserving the ability of users to explore each mention in its original context.
"For example, you may know that a cancer drug affects the behaviour of a certain pathway, but with LION LBD, you may find that a drug developed for a totally different disease affects the same pathway," said Korhonen.
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