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Li C Xue

Showing results (11-20 of 29) with videos related to

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Plos One|May 22, 2014
RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteinsRasna R Walia, Li C Xue, Katherine Wilkins, et al.
Proteins|November 13, 2018
iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutationsCunliang Geng, Anna Vangone, Gert E Folkers, et al.
Biomolecules|January 21, 2023
MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking ConformationsYong Jung, Cunliang Geng, Alexandre M J J Bonvin, et al.
Softwarex|April 14, 2022
iScore: An MPI supported software for ranking protein-protein docking models based on a random walk graph kernel and support vector machinesNicolas Renaud, Yong Jung, Vasant Honavar, et al.
Bioinformatics (Oxford, England)|June 15, 2019
iScore: a novel graph kernel-based function for scoring protein-protein docking modelsCunliang Geng, Yong Jung, Nicolas Renaud, et al.
Frontiers in Immunology|April 8, 2025
Predicting reverse-bound peptide conformations in MHC Class II with PANDORADaniel T Rademaker, Farzaneh M Parizi, Marieke van Vreeswijk, et al.
Briefings in Bioinformatics|March 26, 2016
Template-based protein-protein docking exploiting pairwise interfacial residue restraintsLi C Xue, João P G L M Rodrigues, Drena Dobbs, et al.
Frontiers in Molecular Biosciences|July 21, 2023
Understanding structure-guided variant effect predictions using 3D convolutional neural networksGayatri Ramakrishnan, Coos Baakman, Stephan Heijl, et al.
Cell Reports Methods|April 2, 2026
A high-speed attention network for MHC-bound peptide identification and 3D modelingCoos A B Baakman, Giulia Crocioni, Cunliang Geng, et al.
Frontiers in Immunology|December 25, 2023
PANDORA v2.0: Benchmarking peptide-MHC II models and software improvementsFarzaneh M Parizi, Dario F Marzella, Gayatri Ramakrishnan, et al.
Pageof 3

Showing results (11-20 of 29) with videos related to

Sort By:
Pageof 3
Plos One|May 22, 2014
RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteinsRasna R Walia, Li C Xue, Katherine Wilkins, et al.
Proteins|November 13, 2018
iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutationsCunliang Geng, Anna Vangone, Gert E Folkers, et al.
Biomolecules|January 21, 2023
MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking ConformationsYong Jung, Cunliang Geng, Alexandre M J J Bonvin, et al.
Softwarex|April 14, 2022
iScore: An MPI supported software for ranking protein-protein docking models based on a random walk graph kernel and support vector machinesNicolas Renaud, Yong Jung, Vasant Honavar, et al.
Bioinformatics (Oxford, England)|June 15, 2019
iScore: a novel graph kernel-based function for scoring protein-protein docking modelsCunliang Geng, Yong Jung, Nicolas Renaud, et al.
Frontiers in Immunology|April 8, 2025
Predicting reverse-bound peptide conformations in MHC Class II with PANDORADaniel T Rademaker, Farzaneh M Parizi, Marieke van Vreeswijk, et al.
Briefings in Bioinformatics|March 26, 2016
Template-based protein-protein docking exploiting pairwise interfacial residue restraintsLi C Xue, João P G L M Rodrigues, Drena Dobbs, et al.
Frontiers in Molecular Biosciences|July 21, 2023
Understanding structure-guided variant effect predictions using 3D convolutional neural networksGayatri Ramakrishnan, Coos Baakman, Stephan Heijl, et al.
Cell Reports Methods|April 2, 2026
A high-speed attention network for MHC-bound peptide identification and 3D modelingCoos A B Baakman, Giulia Crocioni, Cunliang Geng, et al.
Frontiers in Immunology|December 25, 2023
PANDORA v2.0: Benchmarking peptide-MHC II models and software improvementsFarzaneh M Parizi, Dario F Marzella, Gayatri Ramakrishnan, et al.
Pageof 3