Warp: a method for neural network interpretability applied to gene expression profiles
This addresses the need for interpretability in neural networks for computational biology, though it appears incremental as a proof of principle.
The authors tackled the problem of interpreting neural networks in gene expression analysis by introducing Warp, a method that recovers meaningful, sample-specific information for given classes and works well on both linearly and nonlinearly separable datasets.
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.