Combining molecular dynamics and machine learning to improve protein function recognition
about
The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applicationsIntegration of Diverse Research Methods to Analyze and Engineer Ca-Binding Proteins: From Prediction to ProductionHigh Resolution Prediction of Calcium-Binding Sites in 3D Protein Structures Using FEATURE.Efficient algorithms to explore conformation spaces of flexible protein loops.Calciomics: integrative studies of Ca2+-binding proteins and their interactomes in biological systems.Predicting Ca2+ -binding sites using refined carbon clusters.Analysis and prediction of calcium-binding pockets from apo-protein structures exhibiting calcium-induced localized conformational changes.Graphlet kernels for prediction of functional residues in protein structures.
P2860
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P2860
Combining molecular dynamics and machine learning to improve protein function recognition
description
2008 nî lūn-bûn
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2008年の論文
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2008年論文
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2008年論文
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2008年論文
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2008年論文
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2008年論文
@zh-tw
2008年论文
@wuu
2008年论文
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2008年论文
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name
Combining molecular dynamics and machine learning to improve protein function recognition
@ast
Combining molecular dynamics and machine learning to improve protein function recognition
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type
label
Combining molecular dynamics and machine learning to improve protein function recognition
@ast
Combining molecular dynamics and machine learning to improve protein function recognition
@en
prefLabel
Combining molecular dynamics and machine learning to improve protein function recognition
@ast
Combining molecular dynamics and machine learning to improve protein function recognition
@en
P2093
P2860
P1476
Combining molecular dynamics and machine learning to improve protein function recognition
@en
P2093
Dariya S Glazer
Randall J Radmer
Russ B Altman
P2860
P304
P50
P577
2008-01-01T00:00:00Z