Open sw1 opened 7 years ago
Generative Adversarial Network
(183 x 224 = 40,992 probes) - they did not discuss how the probes were ordered.
It was interesting that their motivation for using a GAN was to use the probe weights that the Discriminator learned to classify cancer vs. non cancer. It seems to me that the weights would learn real vs. fake and not necessarily have any direct bearing on cancer vs. non cancer. Makes me wonder why a GAN is used here at all - they do try to compare with SVM and logistic regression but results are not very clear.
@gwaygenomics Do you think we should include this in the review, and if so, does it belong in Study (gene expression sub-section) or Categorize?
Agree with categorize label. Not sure about inclusion in review. Generative model plus categorize is really interesting and I think could have a big impact but this article doesn't really use the generator to its potential. I guess I'd say let's see how the categorize section comes together
We also have the potential to stick something like that in the Discussion. I have been thinking about network architectures that are hot in other domains but not yet widely applied in biomedicine. Those may be areas of opportunity in the short term, and GANs could fit in that category.
I'm going to drop this for now from consideration for the categorize section. I think that, given the issues of the paper, it's not quite ready to help us determine whether or not this pushes the field forward.
https://arxiv.org/abs/1612.03211
They use a GAN and compare it to an RBM.