LABORATORY INFORMATICS g
This breaks down into several layers with the cloud infrastructure at the bottom, and then Platform for Science software as the data management infrastructure designed to support scientific workflows. Users can then make use of apps targetted at specific workflows. These apps sit on top of the Platform for Science software. ‘The apps are pre-configured workflows,
so the advantage is that these are implementation accelerators. If you think about genomics, for example, the workflow is pretty prescriptive in terms of sample preparation and handling those samples. Each of those steps in the process can be managed using protocols, whether that is our equipment or other vendors equipment, added Meek. By building out those pre-configured
steps, it helps to drive standardisation and also ensures that they behave in a way that is complimentary with the protocol they are executing,’ said Meek. ‘There are also data analytics apps to help with the further analysis of data beyond just the step-by-step workflows. It is really about accelerating implementation and driving standardisation in that implementation. ‘They are a reflection of your business
process, just as any LIMS or LEN deployment would be. You want to make sure you are mapping the workflow that is happening in the lab into the software, so that it is truly adding value to the customer and accelerating their laboratory workflow,’ said Meek.
Implementing machine learning The applications provided by Thermo allow users to streamline processes, such as sample preparation or common steps, in many workflows such as defined Polymerase Chain Reaction (PCR) experiments or gel electrophoresis and many others. They cover areas with many apps targetted towards genomics or biopharmaceuticals, but there are also apps for things like data analytics which can be used in conjunction with core Thermo software products, such as LIMS or ELN.
‘Machine learning is huge in the life
science healthcare industry. It is able to sift through a lot of data, provide insight and make predictions based on the trained models. It has also shown a lot of potential in areas such as disease diagnosis and even drug discovery. Just after the new year, Google AI announced that they were able to detect breast cancer far better than actual doctors, and last year they had a similar story looking at the diagnosis of prostate cancer,’ said Shrestha ‘AI is definitely positioned to disrupt the life sciences industries to a great extent.
12 Scientific Computing World February/March 2020
”The way customers are using data analytics today, they are using the LIMS capabilities as a central point of aggregation for all of their data”
That is more of a long-term impact but we are seeing the advent of microservices which is the breakdown of large monolithic applications into smaller pieces, so they can scale independently and provide better fault tolerance for the underlying application, provide a greater degree of reliability and also reduce the lead time to deliver features and functionality in software applications to customers,’ said Shrestha. ‘I would say that microservices in the area of software development and AI and ML in terms of addressing these more complex issues in healthcare and life sciences. Those would be two major trends that we are observing.’ For users who want to begin implementing AI and ML techniques with their own laboratory data, Meek notes that a good place to start is developing an infrastructure that can support the necessary stream of data and the contextual metadata needed to support AI. Only by ensuring that the correct data is being stored can scientists hope to get the full value out of their data sources.
For example metadata such as instrument parameters are vital if a computer model is to compare different experiments. ‘The way our customers are using data analytics today is that they are using the LIMS capabilities as a central point of aggregation for all of their data, the SMDS capabilities that help pull all of this together, and then use the APIs that we have to feed that data to ML and other AI applications,’ said Meek. ‘In the same way that we talked about
cloud technologies and companies wanting to make their own decisions about their cloud technology and their infrastructure, we are seeing the same thing from an AI and ML perspective. ‘It is really about partnering with the
right organisations out there that are delivering these technologies, and ensuring we are providing information that is accessible and available for predictive and prescriptive analytics,’ Meek continued. ‘It is not just about data availability but also the completeness of the data. By that, I mean all the associated metadata because ML needs that ancillary data to understand the data model. We are seeing customers capture far more data than they ever did and make sure that they are pulling that data together, so they can inform the data model.’ ‘ML can really be complemented
by cloud computing, because most customers cannot run those heavy processing workloads in on-premise environments. ML and cloud go together very well,’ concluded Shrestha.
@scwmagazine |
www.scientific-computing.com
monkographic/
Shutterstock.com
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28
