An Approach to Developing Benchmark Datasets for Protein Secondary Structure Segmentation from Cryo-EM Density Maps
Author:
Affiliation:
1. Old Dominion University, Norfolk, United States
Funder
NIH
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3584371.3612947
Reference29 articles.
1. DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM
2. Deep convolutional neural networks for detecting secondary structures in protein density maps from cryo-electron microscopy
3. Maddhuri Venkata Subramaniya , S. R. , Terashi , G. and Kihara , D . Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning. Nature methods, 16, 9 (Sep 2019 ), 911--917. Maddhuri Venkata Subramaniya, S. R., Terashi, G. and Kihara, D. Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning. Nature methods, 16, 9 (Sep 2019), 911--917.
4. Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo‐Electron Microscopy Maps
5. A Tool for Segmentation of Secondary Structures in 3D Cryo-EM Density Map Components Using Deep Convolutional Neural Networks
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