LABORATORY INFORMATICS


Applying AI


SOPHIA KTORI COMPLETES HER TWO-PART SERIES ON THE USE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE RESEARCH


Artificial intelligence isn’t all about smarter, more accurate diagnostics, predictive or


personalised medicine, points out Lee Harland, founder and CSO at Cambridge, UK-based SciBite. There are far more fundamental applications. ‘There’s a phenomenal amount of data coming out of laboratory machines, and this is causing data management problems that are undermining the achievable value of that data.’


The availability of vast amounts of


increasingly detailed data has, in parallel, spawned something of a philosophical change in how scientists perceive that data, Harland suggests. ‘Lab work and clinical practices have traditionally been very hands on, and data was a bit of an inconvenience.’ But now that so much R&D and clinical testing is automated, data has become completely central to results and decision making. ‘Speak to the pharmaceutical industry and it’s evident that data is now at the very heart of what they do, and much of their remit is to generate data that drives product development.’


Bringing in AI Using AI to clean up and make sense of messy text-based information means


10 Scientific Computing World December 2018/January 2019


that AI algorithms used downstream to analyse that information can then make full use of what is put in front of them, Harland continues. ‘In a drug discovery environment being able to access and use information on the biology of a drug target, its potential side effects, comparisons with other molecules in the same class, and structural data, depends on the clarity and interoperability of data available in databases, and generated by instrumentation.’ From the user’s perspective, and starting


from first principles, the first stage is knowing where to find your data. Sounds simple, but Harland cites one company that acknowledged holding 148 different databases. ‘And even once you know where your data is, you must know what to search for,’ he says. The painkiller acetaminophen, for example, is known as paracetamol in the UK, but in the US its everyday name is Tylenol. Search for the wrong term and your software may not recognise that the two mean the same thing and two different people may obtain two sets of very different results.’ SciBite is addressing these sorts of first


layer issues using AI as the foundation for tackling the problem of synonymy – paracetamol vs Tylenol – but going far beyond the capabilities of traditional search engines that treat words as just strings of letters, with no intrinsic meaning. Harland said: ‘Many drugs have multiple trade names, for example, and every gene has many different names. It’s one thing generating software that can recognise multiple terms, but with AI we train machines to understand that Tylenol and paracetamol, may be different words, but they are, in effect, the same drug, and once you can train a machine to recognise what


a word “is”, then those words start to have meaning.’


Understanding scientific meaning SciBite’s AI algorithms can be thought of as the plumbing that underpins text-based systems, such as a laboratory notebook or assay registration system. They allow software to understand scientific meaning, Harland suggests. ‘Text itself is pretty much useless to a


computer,’ he said, ‘but turn that text into data, and suddenly it becomes usable content. Think about a published scientific paper, which may contain a huge amount of scientific information, but is completely unusable from an analytical perspective. It’s just a collection of words in a specific order. ‘Take that same paper and run it through


our software, however, and it outputs data that is interoperable with other datasets, and can be turned into more structured, machine-readable data that will work with downstream analytical algorithms.’ At the heart of SciBite’s semantic


analytics software suite is TERMite (term @scwmagazine | www.scientific-computing.com


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