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; Pasquali, Federica; Brahnam, Sheryl; Lumini, Alessandra; and Axenopoulos, Apostolos, "Convolutional neural networks for 3d protein classification" (2020). Articles by College of Business Faculty. 545.
https://bearworks.missouristate.edu/articles-cob/545
Journal Title
Intelligent Systems Reference Library