15 November 2019
Ben Paice By Ben Paice

Getting to the Heart of Data, Bioinformatics and Personalised Care.

Data is the new oil of the 21st Century.”

2006, Clive Humby, Mathematician & Architect of the Tesco Clubcard


It’s quite a bold claim, on a topic that’s far too broad to speak about in this manner. Reigning in Clive’s words a little, I agree that data is everything... in healthcare.

Heading into the world of personalised care, data insights are providing us with more effective, targeted and preventative treatments for patients. 

The dependence on data is so strong, that discovering these actionable insights for personalised care has led to the emergence of a new area of study and investment, bioinformatics.

Bioinformatics is the science of storing, retrieving and analysing large amounts of biological information, like DNA/protein sequences. It’s a highly interdisciplinary field involving many different types of specialists, including biologists, molecular life scientists, computer scientists and mathematicians.

This is an essential piece of the personalised care puzzle, with the biological data that bioinformatics provides helping identify patients susceptible to certain diseases, which are then proactively treated before the patient falls ill. 

Data is a key part of bioinformatics, meaning that any integration with AI is an exciting prospect. This could lead to the eradication of human error, whilst also making processes quicker and more automated. 

The algorithms required for a variety of bioinformatics processes are becoming increasingly complex, with the development of computers and software struggling to keep up. This is where AI can step in to bridge the gap. 

Integrating AI into Bioinformatics has already led to an array of developments in the field, including faster image analysis, which can result in much earlier diagnosis of multiple types of cancer. 

The major advantage of employing AI within bioinformatics for genome annotation is its ability to automatically identify patterns in a huge amount of data. 

Using data, advanced AI identifies these patterns through machine learned software and algorithms that it can develop far quicker and more accurate than any human. Advanced AI can also learn from mistakes and previous actions, resulting in a complete lack of errors for algorithms or types of software which are adapted to different uses.

Using AI can also lead to advantages in the collection and storage of data from various research and activities, such as gene expression. Instructing AI to collect data, store it, and present data eliminates the chance of human coding ad input error, which can result to errors in the database. This can have a knock-on effect for other experiments or findings where this database is referred to.

With so many advantages, there’s some fear among bioinformaticians that AI could make their role in bioinformatics redundant. However, I don’t believe this is a cause for concern. Their skills are transferable and will be invaluable in the complex process of AI integration. Furthermore, there’s still some apprehension and trust issues surrounding AI, meaning that for now a manned presence will be preferred. But don't let this point detract from the fact that AI intergration will almost certainly lead to huge time and cost savings. 

As I said at the start, I’m not getting into the ‘new oil of the 21st century’ debate... this is a quick reflection of what I’m seeing in my market. For healthcare, the future is clearly data-driven and a move to personalised care needs to be supported by bioinformatics and its integration with AI.


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Ben Paice

Ben Paice is a Business Manager, leading a team of diagnostic specialists with a global focus. He specialises specifically in data science & bioinformatics and today is responsible for handling some of Charlton Morris’ most important accounts.


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