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.

我们证明了扭曲的原理，这是一种解释神经网络在基因表达分析上内部工作的方法，扭曲是获得神经网络内部工作理解的一种有效途径，我们证明了扭曲能够从样本特定的个人角度恢复给定类别的有意义信息，并发现扭曲在线性和非线性可分数据集上都表现良好，这些令人鼓舞的结果表明，在计算生物学中，扭曲有成为神经网络可解释性的答案的潜力。

Warp：基于神经网络的基因表达数据可解释性方法

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

Kernel classifiers and regressors designed for structured data, such as
sequences, trees and graphs, have significantly advanced a number of
interdisciplinary areas such as computational biology and drug design.
Typically, kernels are designed beforehand for a data type which either exploit
statistics of the structures or make use of probabilistic generative models,
and then a discriminative classifier is learned based on the kernels via convex
optimization. However, such an elegant two-stage approach also limited kernel
methods from scaling up to millions of data points, and exploiting
discriminative information to learn feature representations.
We propose, structure2vec, an effective and scalable approach for structured
data representation based on the idea of embedding latent variable models into
feature spaces, and learning such feature spaces using discriminative
information. Interestingly, structure2vec extracts features by performing a
sequence of function mappings in a way similar to graphical model inference
procedures, such as mean field and belief propagation. In applications
involving millions of data points, we showed that structure2vec runs 2 times
faster, produces models which are $10,000$ times smaller, while at the same
time achieving the state-of-the-art predictive performance.

提出了一种名为 structure2vec 的基于嵌入潜在变量模型的结构化数据表征方法，利用判别性信息进行特征空间学习，在数百万数据点的应用中运行速度是传统方法的两倍，模型则小 $10,000$ 倍，同时达到了最先进的预测性能。