Capsule Networks for Protein Structure Classification and Prediction
arXiv:1808.07475
Abstract
Capsule Networks have great potential to tackle problems in structural biology because of their attention to hierarchical relationships. This paper describes the implementation and application of a Capsule Network architecture to the classification of RAS protein family structures on GPU-based computational resources. The proposed Capsule Network trained on 2D and 3D structural encodings can successfully classify HRAS and KRAS structures. The Capsule Network can also classify a protein-based dataset derived from a PSI-BLAST search on sequences of KRAS and HRAS mutations. Our results show an accuracy improvement compared to traditional convolutional networks, while improving interpretability through visualization of activation vectors.
References in corpus (6)
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Methods for Interpreting and Understanding Deep Neural Networks
- AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
- Grad-CAM: Why did you say that?
- Feature Importance Measure for Non-linear Learning Algorithms
- Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction