Title: Warp: a method for neural network interpretability applied to gene expression profiles,
Abstract: We show a proof of principle for warping, a method to interpret the inner
working of neural networks in the context of gene expression analysis. Warping
is an efficient way to gain insight to the inner workings of neural nets and
make them more interpretable. We demonstrate the ability of warping to recover
meaningful information for a given class on a samplespecific individual basis.
We found warping works well in both linearly and nonlinearly separable
datasets. These encouraging results show that warping has a potential to be the
answer to neural networks interpretability in computational biology.