Nasrin Akhter
Nasrin Akhter
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Amarda Shehu
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Improved protein decoy selection via non-negative matrix factorization
Deep Ranking in Template-free Protein Structure Prediction
Reconstruction and Decomposition of High-Dimensional Landscapes via Unsupervised Learning
Decoy selection for protein structure prediction via extreme gradient boosting and ranking
Anomaly Detection-based Recognition of Near-Native Protein Structures
From Molecular Energy Landscapes to Equilibrium Dynamics via Landscape Analysis and Markov State Models
Identifying Near-Native Protein Structures via Anomaly Detection
Non-negative matrix factorization for selection of near-native protein tertiary structures
Unsupervised and supervised learning over the energy landscape for protein decoy selection
Graph-Based Community Detection for Decoy Selection in Template-Free Protein Structure Prediction
Connecting Molecular Energy Landscape Analysis with Markov Model-based Analysis of Equilibrium Structural Dynamics
Unsupervised Learning of Conformational States Present in Molecular Dynamics Simulation Data for Summarization of Equilibrium Conformational Dynamics
Unsupervised Learning for Decoy Selection in Protein Structure Prediction
Improved Decoy Selection via Machine Learning and Ranking
From Mutations to Mechanisms and Dysfunction via Computation and Mining of Protein Energy Landscapes
Community Detection for Decoy Selection in Template-free Protein Structure Prediction
An Energy Landscape Treatment of Decoy Selection in Template-free Protein Structure Prediction
Analysis of Energy Landscapes for Improved Decoy Selection in Template-free Protein Structure Prediction
Learning Organizations of Protein Energy Landscape: An Application on Decoy Selection in Template-Free Protein Structure Prediction
Reconstructing and Decomposing Protein Energy Landscapes to Organize Structure Spaces and Reveal Biologically-active States
From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-free Protein Structure Prediction
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