The Future of Life Science is Knowledge Graphs

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The Future of Life Science is Knowledge Graphs

As data and metadata from experiments in life science companies grow larger, it is becoming harder and harder to understand and interpret multi-faceted data. In the last 20 years alone, the amount of data points derived from experiments has gone from around three megabytes to one to four thousand terabytes.  Milestones are being delayed due to the massive amounts of data that needs to be computed and analyzed. The reality is that we will reach a point where scientists are unable to comprehend their own research findings. The question is, what can help scientists understand and interpret an overwhelming mass of data? The answer: Knowledge Graphs.

So what are knowledge graphs? Knowledge Graphs connect complex relationships between different types of entities through visualization. Simply put, Knowledge Graphs are like a digital, customizable evidence board used in cop shows with a red string tied everywhere. But, instead of trying to prove whodunnit, you can visualize connections between any kind of metadata. From linking data about existing chemical compounds, seeing parent-children relationships between samples, and mapping out your experiments from procedures, and samples to results, Knowledge Graphs make it easier to spot previously overlooked connections and data discrepancies.

You could be running a proteomics workflow, and creating hundreds of derivatives from your samples to create proteins and peptides. Using a knowledge graph, you can easily identify parent-child relationships and feed the information into your data science algorithm with clean data. 

If you have multiple runs for the same experiments with different results, you can easily link them together to identify what experiments were successful based on what protocols. 

With Scispot’s Knowledge Graph platform, not only can you create your own knowledge graphs, but you can prioritize and highlight relationships and nodes. Scispot structures your data and makes it machine-actionable, guaranteeing data integrity. Having clean data opens opportunities to use your metadata for computational research and machine learning.

Scispot’s Knowledge Graph equips data engineers and scientists to better see how to interpret their metadata for breakthrough findings and accelerate milestones. It can save data engineers hours of frustration by providing clean data and explainable research. Using Scispot’s Knowledge Graphs, biotech startups and scaleups can push themselves to new heights with fresh insights. Instead of your data analytics simply tabulating and retrieving data, you can create real-world meanings and relations. 

Book a demo with a Scispot team member to learn how we can help you connect the dots and use Knowledge Graphs to make explaining your experiment data a breeze. 

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