Title: Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific Data,
Abstract: Machine learning algorithms such as linear regression, SVM and neural network
have played an increasingly important role in the process of scientific
discovery. However, none of them is both interpretable and accurate on
nonlinear datasets. Here we present contextual regression, a method that joins
these two desirable properties together using a hybrid architecture of neural
network embedding and dot product layer. We demonstrate its high prediction
accuracy and sensitivity through the task of predictive feature selection on a
simulated dataset and the application of predicting open chromatin sites in the
human genome. On the simulated data, our method achieved high fidelity recovery
of feature contributions under random noise levels up to 200%. On the open
chromatin dataset, the application of our method not only outperformed the
state of the art method in terms of accuracy, but also unveiled two previously
unfound open chromatin related histone marks. Our method can fill the blank of
accurate and interpretable nonlinear modeling in scientific data mining tasks.