Can Machine Learning Fill In The Data Gap For Understanding Cannabis?
There exists a diverse array of chemicals believed to quietly interact – a phenomenon known as the ‘entourage effect’ — influencing how each unique cannabis strain makes people feel.
Surprisingly, we have very little data about lesser-known compounds, according to the University of Colorado Boulder research. It tells us that applying advanced data science techniques in this area could give us a better idea of how this plant works. All the plants are not the same. Different flavonoids and terpenes could bring new smell, taste, and texture, offering slightly different effects – some could be stimulating while others could be sedating or relaxing. We can only know this by measuring the chemicals.
On assessing the data available on seven different cannabinoids, the researchers found that about 38% of cultivars were missing data about CBD and only 1.4% of cultivars were missing data about THC, with some being never measured at all. With data science methods like machine learning, we could uncover more. While this will help tremendously in having a better understanding, it could only be done right with widespread cannabis industry collaboration. If we have all the data we need, custom products could be developed confidently for medical use based on the complex entourage effect of interacting compounds.
Read Full Article: https://www.colorado.edu
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