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Big Data Analytics and Artificial Intelligence: 7 Design Principles to Empower Life Science Researchers

Life sciences research is increasingly relying on big data analytics to drive scientific discovery. One of the contributing factors to this trend is the fact that researchers are confronted with a rapidly growing body of scientific literature and databases required to interpret experimental outcomes.

Take, for instance, the area of preclinical target/biomarker discovery and validation. To assess whether a novel gene may play a causative role in a certain disease pathology, many aspects need to be considered including its interaction with(in) dysregulated genes, disease variants, metabolic interactions, pathways, cellular and molecular processes, organ and tissues functions and disease phenotypes.

preclinical-target-biomarker-discovery-validation

Each one of these aspects requires a significant amount of data to be gathered before conclusions can be drawn. Manually collecting this much information within a reasonable timeframe is unfeasible without using some type of big data analytics approach. Most researchers, however, lack the necessary IT skills and need to involve big data experts or bioinformaticians to support them in this effort.

The life sciences researcher as a ‘casual user’ of IT

The value of leveraging big data analytics would be greatly increased if this reliance on others could be minimised. Achieving this requires an approach that fundamentally matches the needs, interests and abilities of this type of user.

Most biologists and researchers are so called ‘casual users of IT’. A ‘casual’ user just wants to get a job done and has no interest in mastery of a tool. Being a casual user doesn’t mean you are a novice or an infrequent user. Most researchers have been using Excel for years and do so on a regular basis, but only have a ‘casual’ understanding of how it can be used. In essence, casual users need to draw value from any application they use intuitively, requiring little effort, otherwise they will lose interest and move on.

7 design principles to empower life sciences researchers

In order to support researchers, a number of key design principles are essential to the Euretos AI platform. These include:

  1. Building an ontological and semantic foundation. This way, the platform adheres to the fundamental working of biology, including the many synonyms used in various sources. Simply said; it enables the platform to ‘speak’ to the researcher from a common background.
  2. Using only known form factors, such as search engines (e.g. Google), spreadsheets (e.g. Excel) and visualisations (e.g. heatmaps). Users already have these types skills and will therefore be able to intuitively use them.
  3. Providng relevant (i.e. biological) information at every step. This may sound obvious but is hard to achieve as quite often complex information needs to be presented and managed. We also believe that to achieve this, a focused approach needs to be taken, which in our case is target/biomarker discovery and validation.
  4. Being proactive towards the user and providing relevant suggestions on what to do as a next step. Do not make the ‘next step’ hidden behind some menu item. Also suggest what to focus on: What is potentially novel? What is the most likely key player? This is where Artificial Intelligence comes into play to actively engage with the user.
  5. Interpreting user intent in order to be proactive. Natural Language Processing is then required to parse from the inputs what the user is looking for and, based on that analysis, provide relevant responses and suggestions.
  6. Using transparent scoring models to drive proactivity. Transparency and biological relevance is essential so users can understand the score and why it is relevant. We have developed for instance a gene-disease association score that is fully based on different types of biological interactions where each interaction can then be assessed by the user so there is complete insight into what is driving the score.
  7. Ensuring relevant literature (including clinical trial data and patents), is always close to hand. Researchers want to read literature. In the end this is their preferred way to assess and process information.

Putting the power of bioinformatics and big data analytics in the hands of the researcher is an ongoing process where platforms increasingly engage with users as intelligent entities: understanding the user’s needs and intentions and entering into a relevant dialogue. This can always be improved upon, but as long as the essential elements are in place, progress will be made. Euretos look forward to keep pushing these boundaries to further empower researchers, especially in the area of preclinical target/biomarker discovery and validation.

Arie Baak260

 

Arie Baak is the Co-Founder of Euretos. Arie recently presented at the Global Pharma R&D Informatics Congress in Portugal.


If you found this interesting, you may also like ‘Not Another IT System: Communities Of Practice In Pharma

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