LINA: A Linearizing Neural Network Architecture for Accurate First-Order and Second-Order Interpretations.

ABSTRACT: While neural networks can provide high predictive performance, it was a challenge to identify the salient features and important feature interactions used for their predictions. This represented a key hurdle for deploying neural networks in many biomedical applications that require interpretability, including predictive genomics. In this paper, linearizing neural network architecture (LINA) was developed here to provide both the first-order and the second-order interpretations on both the instance-wise and the model-wise levels. LINA combines the representational capacity of a deep inner attention neural network with a linearized intermediate representation for model interpretation. In comparison with DeepLIFT, LIME, Grad*Input and L2X, the first-order interpretation of LINA had better Spe

SUBMITTER: Badre A 

PROVIDER: S-EPMC9032252 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature