LABORATORY INFORMATICS
output in Excel spreadsheets or flat-files. Merging multiple files in different formats becomes challenging and it is difficult, at that stage, to ask contextual questions of such data.’
More than just digitising data Addressing the deficit is much more than just digitising experimental results, Kloepper continues. Digitising data is a fundamental goal for every industry, from retail to manufacturing. ‘But in the life sciences you also have to keep up with a constantly evolving research environment that is developing new assays and new ways of using existing assays. Any intelligent digital platform needs to be able to evolve alongside that research process.’ The Aigenpulse platform employs machine learning to engage that analytical process so that scientists can have more confidence in their interpretation of those analyses. It’s a concept for which about 80 per cent of the work involves getting the data into the right structured, contextual environment and the other 20 per cent is the machine learning to derive insight from that data, Kloepper notes. The two must go hand in hand, and there’s little point in asking your algorithm to answer a question if you don’t have reliable high-quality data available and structured appropriately. Provide your algorithms with a robust
dataset and the job becomes more seamless and accurate. Importantly, the Aigenpulse platform can work with data in several formats across a company, from laboratory information management systems (LIMS), electronic laboratory notebooks (ELNs) and other data repositories, to results of sample analyses captured using proprietary software or output in proprietary data formats. ‘As well as contextualising that data, the Aigenpulse platform is designed to remove noise from such data, which allows the software to
”AI isn’t going to make scientists redundant, but it will help scientists find the best answers to the questions they ask. In the next 10-20 years we will see every scientist being able to use machine learning algorithms as routinely as they carry out common laboratory assays today”
www.scientific-computing.com | @scwmagazine
Advanced analytics can empower decision making when the algorithmic output is packaged in tools and software which are easy to use and interpret
more accurately model patterns.’ Often such processes are about 95 per
cent automated, but interfaces built into the software allow scientists to validate data to support the algorithm, Kloepper states. ‘Our control vocabularies harmonise data, spanning gene expression datasets, common assays such a ELISA and FACS, or mass spectrometry analyses.’ The software then looks at the structured information from every perspective, including disease, targets and compounds, so that the algorithm can learn and derive answers to specific questions set by scientists and biostatisticians.
Persistence of analytics Try and do that with other platforms, or when all you have is PDFs and Excel files, and there will always be issues with data mapping and data matching, says Satnam Surae, chief product officer at Aigenpulse. ‘We enable that persistence of analytics, which extends to running a machine learning pipeline on all of the data available to the whole company if necessary, or scaling things down to the level of individual experiments. Importantly, by retaining the data in the state it was output and at the time it was derived, you can compare models at different time points.’ The Aigenpulse platform provides
scientists with a web-interface for their data and analytical results, which reduces
complexity and optimises usability. ‘Scientists can pull up the bits of data that they want, select the model or method that they want to run, put in the parameters, and click to set the analysis running,’ Surae adds. ‘The back end does all the work, and the output is displayed at the front end, in the most appropriate form, and in a matter of a few clicks.’ The Aigenpulse platform can be
integrated into existing IT infrastructure on clients’ premises or in the cloud, and it can be precisely configured to match the requirements of each client. ‘Our aim is to help scientists derive greater insights into their research, through their data generated, and support them to ultimately develop better drug candidates faster and with less attrition,’ Kloepper states. Concerns that intelligent software will put jobs at risk are unfounded, he believes. ‘AI isn’t going to make scientists redundant, but what it will do is help scientists find the best answers to the questions that they ask. In the next 10 to 20 years we will see every scientist being able to use machine learning algorithms as routinely as they carry out common laboratory assays today. ‘Scientists are very open to new
technologies, and AI-driven tools will enable them to be more data driven in their decisions. And, ultimately, this will help industry develop more effective, safer drugs, faster.’
December 2018/January 2019 Scientific Computing World 13
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