[MrMoenty]

This is an active research area that one of my former professors is involved in. Things are not as simple as you hope they would be.

You're optimizing in a space of millions of discrete dimensions (one for each base pair), with little knowledge about independencies. This is in contrast to tasks like image recognition, where we can make use of the spacial structure of the pixels to build effective models like convnets.

Additionally, medical datasets, especially ones with genetic data, rarely contain more than a few hundred datapoints, which is not where you want to be for deep learning.

Machine learning is still useful in this area, especially to find genes or gene combinations with high correlation to certain diseases. But we are very far away from having a model that maps genome -> healthiness.