Residue Assignment in Crystallographic Protein Electron Density Maps With 3D Convolutional Networks

Residue Assignment in Crystallographic Protein Electron Density Maps With 3D Convolutional Networks

This work proposes a neural network architecture, called 3D FC-DenseNet, for assigning amino acid labels to X-ray crystallographic electron density maps without relying on the amino acid sequence of proteins. The 3DFC-DenseNet is able to treat the task as a 3D semantic segmentation problem, assignin...

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מידע ביבליוגרפי
Main Authors: Akos Godo, Kota Aoki, Atsushi Nakagawa, Yasushi Yagi
פורמט: Article
שפה:English
יצא לאור: IEEE 2022-01-01
סדרה:IEEE Access
נושאים:
Neural networks
image segmentation
X-ray diffraction
proteins
amino acids
גישה מקוונת:https://ieeexplore.ieee.org/document/9726158/

אינטרנט

https://ieeexplore.ieee.org/document/9726158/

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