[Real_S]

Interesting point about Wigner, but about the article...

This study does not examine phenotypes, but may be applied to them somehow. Instead:

>we use simulation to show that CNNs can leverage images of aligned sequences to accurately uncover regions experiencing gene flow between related populations/species, estimate recombination rates, detect selective sweeps, and make demographic inferences

I believe this works well because the sorting of the data (Fig. 2) introduces phylogenetic information into the image to be analyzed. This reminds me of neighbor joining [0], but has some differences. Without this ordering, their method does not work as well.

0)https://en.wikipedia.org/wiki/Neighbor_joining

[longtom]

> sorting of the data

Perhaps the main advantage is that it can filter out non-adaptive, non-functional (noisy) mutations this way by simply averaging similar genomes? In that case, the rows of the learned M x N kernels should be nearly identical and one could have simply averaged M data rows at a time and fed it to the 1D CNN.

What other phylogenetic information could possibly be inferred?

Edit: It could also be thought as data augmentation as it effectively creates novel inputs each time. IIRC there was also a technique for hardening against adversarial examples which simply fed the network averaged datapoints along with the original data.