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We show a proof of principle for warping, a method to interpret the innerworking of neural networks in the context of gene expression analysis. Warpingis an efficient way to gain insight to the inner workings of neural nets andmake them more interpretable. We demonstrate the ability of warping to recovermeaningful information for a given class on a samplespecific individual basis.We found warping works well in both linearly and nonlinearly separabledatasets. These encouraging results show that warping has a potential to be theanswer to neural networks interpretability in computational biology.

Warp: a method for neural network interpretability applied to gene expression profiles