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I know that this is going to sound "flavor of the day," but seeing as how genetics is essentially a large data set of variables and we aren't certain about whic
by chacham15 7y ago
I know that this is going to sound "flavor of the day," but seeing as how genetics is essentially a large data set of variables and we aren't certain about which of then are indicative of cancer, doesn't this seem to be very strongly correlated with the problem that most ML/deep learning tries to solve?
- asdff 7y agoGenetics = statistics.
- abhikshah 7y agoML is fairly common in genomics, but for identifying predictive variables for cancer status, it's difficult. The training set is a matrix where rows are people (where some have cancer and some don't) and columns are genomic features (mutation, methylation, etc). You can easily have hundreds of thousands of features but getting even a thousand cancer patients enrolled in a study and sequenced is expensive and slow. So, even though there are many "AI in biotech" companies out there, for predicting cancer status, most eventually end up hand crafting a small number of features based on extensive knowledge of cancer biology. The ML model tends to be simple and far less important than the features.