Title
Convolutional neural networks for 3d protein classification
Abstract
The main goal of this chapter is to develop a system for automatic protein classification. Proteins are classified using CNNs trained on ImageNet, which are tuned using a set of multiview 2D images of 3D protein structures generated by Jmol, which is a 3D molecular graphics program. Jmol generates different types of protein visualizations that emphasize specific properties of a protein’s structure, such as a visualization that displays the backbone structure of the protein as a trace of the Cα atom. Different multiview protein visualizations are generated by uniformly rotating the protein structure around its central X, Y, and Z viewing axes to produce 125 images for each protein. This set of images is then used to fine-tune the pretrained CNNs. The proposed system is tested on two datasets with excellent results. The MATLAB code used in this chapter is available at https://github.com/LorisNanni.
Department(s)
Information Technology and Cybersecurity
Document Type
Article
DOI
https://doi.org/10.1007/978-3-030-42750-4_9
Publication Date
1-1-2020
Recommended Citation
Nanni, Loris, Federica Pasquali, Sheryl Brahnam, Alessandra Lumini, and Apostolos Axenopoulos. "Convolutional Neural Networks for 3D Protein Classification." In Deep Learners and Deep Learner Descriptors for Medical Applications, pp. 237-250. Springer, Cham, 2020.
Journal Title
Intelligent Systems Reference Library
