AI System may Accelerate Search for Cancer Discoveries

Searching through the mountains of published cancer research could be made easier for scientists, thanks to a new AI system. The system, called LION LBD and developed by computer scientists and cancer researchers at the University of Cambridge, 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. The results are reported 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. 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 who led the development of LION LBD in collaboration with Dr Masashi Narita at Cancer Research UK Cambridge Institute and Professor Ulla Stenius at Karolinska Institutet in Sweden. "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."

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.

LION LBD is the first system developed specifically for the needs of cancer research. It has a particular focus on the molecular biology of cancer and uses state-of-the-art machine learning and natural language processing techniques, in order to detect references to the hallmarks of cancer in the text. Evaluations of the system have demonstrated its ability to identify undiscovered links and to rank relevant concepts highly among potential connections.

The system is built using open data, open source and open standards, and is available as an interactive web-based interface or a programmable API.

The researchers are currently working on extending the scope of LION-LBD to include further concepts and relations. They are also working closely with cancer researchers to help and improve the technology for end users.

The system was developed in collaboration with University of Cambridge Language Technology Lab, Cancer Research UK Cambridge Institute, and Karolinska Institutet in Sweden, and was funded by the Medical Research Council.

Sampo Pyysalo, Simon Baker, Imran Ali, Stefan Haselwimmer, Tejas Shah, Andrew Young, Yufan Guo, Johan Högberg, Ulla Stenius, Masashi Narita, Anna Korhonen.
LION LBD: a literature-based discovery system for cancer biology.
Bioinformatics, doi: 10.1093/bioinformatics/bty845.

Most Popular Now

Apple Health Records Available for Allsc…

Allscripts (NASDAQ: MDRX) announced that Apple Health Records is now available for Allscripts Sunrise™, TouchWorks® and Professional EHR™ clients and their patients. Health Records brings together hospitals, clinics and the...

Philips Signs Agreement to Create Taiwan…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, today announced that Taipei Veterans General Hospital (TPVGH) will utilize the Philips IntelliSite Pathology Solution to transform its...

Robotic Thread is Designed to Slip throu…

MIT engineers have developed a magnetically steerable, thread-like robot that can actively glide through narrow, winding pathways, such as the labrynthine vasculature of the brain. In the future, this robotic...

St Helens and Knowsley Advance with Ambi…

St Helens and Knowsley Teaching Hospitals NHS Trusthas successfully gone live with System C’s CareFlow Vitals as part of its ambitious strategy to accelerate digitisation and become a digital exemplar...

Machine Learning Improves the Diagnosis …

Researchers from Charité - Universitätsmedizin Berlin and the German Cancer Consortium (DKTK) have successfully solved a longstanding problem in the diagnosis of head and neck cancers. Working alongside colleagues from...

Experimental Validation Confirms the Abi…

Insilico Medicine, a global leader in artificial intelligence for drug discovery, today announced the publication of a paper titled, "Deep learning enables rapid identification of potent DDR1 kinase inhibitors," in...

Using a Smartphone to Detect Norovirus

A little bit of norovirus - the highly infectious microbe that causes about 20 million cases of food poisoning in the United States each year - goes a long way...

Computer Model could Help Test New Sickl…

A team of Brown University researchers has developed a new computer model that simulates the way red blood cells become misshapen by sickle cell disease. The model, described in a...

The Future of Mind Control

Electrodes implanted in the brain help alleviate symptoms like the intrusive tremors associated with Parkinson's disease. But current probes face limitations due to their size and inflexibility. "The brain is...

Medical Informatics Europe Conference 20…

28 April - 1 May 2020, Geneva, Switzerland. The European Federation of Medical Informatics (EFMI) presents the 30th Medical Informatics Europe conference (MIE) at the Geneva International Conference Center (CICG). This...