In Child Autism microbiome over time – Part 1 , I reviewed the predicted symptoms for each samples and saw a regular pattern. As a result, I wrote some code to cluster the taxa from most common predicted symptoms… in english, identified the bacteria that appears to be causing most of the regular predictions.
To get to this new report, on the samples page, click [Compare Samples to Each Other]
On the next page, select the samples to be included and click [Probable Symptoms Cluster]
The next page does the analysis automatically, showing the common symptoms and then ranking the bacteria matching these symptoms
Just check the ones that you wish to include. My rule of thumb is to go down to 1/2 of the highest value (i.e. 16.5 or higher, since the high is 33). Then create the custom profile. At this point it will evaluate all of the samples and add this hand picked to the best sample. (You may wish to delete other hand picked taxas before doing this — so you can find it!)
You can view the choices if you wish
At this point, we are ready to try suggestions
What happens here is not unexpected. Because we are dealing with a different selection of bacteria, suggestions may contradict, others in agreement with Child Autism microbiome over time – Part 1
- Recommend by both
- On one but not the other (to take)
- Bifobacterium Bifidum – take
- bifidobacterium longum bb536 (probiotics) – take (Note this is a specific strain!)
There is no easy solution.
- Items recommended by both or avoid by both are the easy part.
- When there is disagreement, I usually opt to omit them unless there is a specific reason to include.
- If it is one one but not the other, I am inclined to go with the suggestion.
This is artificial intelligence and that usually mean that it can be tuned or adjusted. Whether it makes the results better or worst may be subjective.
A Second Child
There is another mother with a boy with autism who has also been doing regular samples. To my surprise (and likely my AI’s delight!!!) we came up with similar results for symptoms
This will likely work with many different conditions. I tried it for myself across all of my samples (in remission of ME/CFS and in relapse) as shown below… Needless to say, the AI did an awesome job!
We do not want to work from a single sample. We want to take regular samples to quiet down random noise in the microbiome and identify the masterminds behind any issues.