LABORATORY INFORMATICS


Recent research at Stanford University has indicated that since the 1930s, the effective number of researchers at work has increased by a factor of 23, but annual growth in productivity has declined. As a result, new ideas are becoming more expensive to find. We are supporting scientists by


extracting facts from the scientific literature, and adding data from other sources, to highlight existing knowledge. Building on this, we are focussing on how AI can be made efficient with approaches such as transfer learning to identify new knowledge. At the same time we’re also working to make AI software tools easier to understand. In the future, I see scientists working


together collaboratively across human social networks, alongside AI. They will benefit from broad integrated cross- domain networks of linked facts, which will allow them to draw inferences and identify patterns from the use of machine


Jabe Wilson


commercial clients; much of what we do is naturally confidential, however our focus is on identifying and linking data to create networks or graph databases of facts that can then be used to answer questions. A big part of this is tidying up the raw data to be able to link facts together. Once you have clean data it is possible


to build analytical models with which to make predictions. We do this in many diverse areas, examples include identifying drugs to repurpose, in order to cure diseases without current treatments; identifying biomarkers that might be used as an early indicator of a disease progression; determining whether a chemical compound might be toxic or therapeutic when used as a drug. One of the most exciting data projects


we are working on at the moment is with a UK-based charity, Findacure. We are helping them find alternative treatment options for rare diseases such as congenital hyperinsulinism by offering our informatics expertise, and giving them access to published literature and curated data through our online tools, at no charge.


www.scientific-computing.com | @scwmagazine We are also supporting The Pistoia


Alliance, a not-for-profit group that aims to lower barriers to collaboration within the pharmaceutical and life science industry. We have been working with its members to collaborate and develop approaches that can bring benefits to the industry. We recently donated our Unified Data Model to the Alliance; with the aim of publishing an open and freely available format for the storage and exchange of drug discovery data. I am still proud of the work I did with them back in 2009 on the SESL project (Semantic Enrichment of Scientific Literature), and my involvement continues as part of the special interest group in AI.


What are your predictions for the industry for the next 10 years? We are going to see a radical change as AI techniques both become more productive and the tools they deliver become more transparent and user friendly. This will become increasingly important if we are going to overcome the productivity crises that many disciplines of science and research are experiencing.


”We are going to see a radical change as AI techniques become more productive”


learning. Working collaboratively with AI has been described variously as ‘centaur science’ and the use of ‘symbiotic technology’. These techniques offer the ability to aim AIs at problems we are interested in solving, and having the means to understand and interpret the answers the AIs are giving us. A key development Elsevier is looking at is called AI neuroscience, where we are trying to build tools that look inside the black box of deep learning models and work out how an individual decision is made. Overall, these advances should lead to a reverse in the reported productivity crisis in science and R&D, and improve outcomes for humanity by solving the problems we face globally in diverse areas – from antibiotic resistance, to environmental degradation and climate change.


Interview by Tim Gillett December 2017/January 2018 Scientific Computing World 13


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