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Saturday, March 13, 2021

Considering Learnability: Brainome

 Was just introduced to a new company:    Brainome    An example problem addressed,  indicating breadth and possibilities :  

" ... We've been working with a genomics research group to analyze the cancer atlas which aggregates gene expression data from patients diagnosed with 33 different kinds of cancers. We combined the cancer atlas with normal (healthy) samples from the GTEx database to create a 34 class / 21,000 column / 11,000 row data set. The goal is to find genes that are consistently over-expressed in cancer patients and, so far, we've identified ~3 dozen that are good candidates for further testing. The long term goal is an early detection blood test for certain types of cancer.  ... " 

Measure and improve the learnability of your data

Do I have enough data?

Do I have the right data features?

Which of my data features are the most impactful?

Will my model overfit?

What kind of ML model will work best with my data?

What is my accuracy vs generalization curve?

Worth a look, See their Blog      And FAQ:  

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