A machine learning approach for the identification of protein secondary structure elements from electron cryo-microscopy density maps

Biopolymers

Volumn 97, Issue 9, 2012, Pages 698-708

  Si, Dong ^a   Ji, Shuiwang ^a   Nasr, Kamal Al ^a   He, Jing ^a  

^a Old Dominion University   (United States)

Author keywords

beta sheet; density map; electron cryo microscopy; helix; local structure tensor; machine learning; protein; secondary structure

Indexed keywords

BACKBONE STRUCTURES; BETA-SHEET; DATA BANK; DENSITY MAPS; DETECTION ACCURACY; HELIX; IDENTIFICATION OF PROTEINS; LEARNING APPROACH; LOCAL STRUCTURE TENSORS; PROTEIN DENSITY; SECONDARY STRUCTURE ELEMENTS; SECONDARY STRUCTURES;

DATABASE SYSTEMS; ELECTRON MICROSCOPES; ELECTRON MICROSCOPY; MEDICAL IMAGING; PROTEINS;

LEARNING SYSTEMS;

ALPHA HELIX; BETA SHEET; CONFERENCE PAPER; CRYOELECTRON MICROSCOPY; DENSITY; ELECTRON MICROSCOPY; MACHINE LEARNING; PROTEIN SECONDARY STRUCTURE; SENSITIVITY AND SPECIFICITY;

ARTIFICIAL INTELLIGENCE; CRYOELECTRON MICROSCOPY; PROTEIN STRUCTURE, SECONDARY;

EID: 84862874104     PISSN: 00063525     EISSN: 10970282     Source Type: Journal    
DOI: 10.1002/bip.22063     Document Type: Conference Paper

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