about
SPINE-D: accurate prediction of short and long disordered regions by a single neural-network based methodImproving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templatesConsensus scoring for enriching near-native structures from protein-protein docking decoysA knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexesFast and accurate non-sequential protein structure alignment using a new asymmetric linear sum assignment heuristicHighly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteinsBEST: improved prediction of B-cell epitopes from antigen sequencesProtein flexibility prediction by an all-atom mean-field statistical theory.LEAP: highly accurate prediction of protein loop conformations by integrating coarse-grained sampling and optimized energy scores with all-atom refinement of backbone and side chainsDirect prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profilesAssessing secondary structure assignment of protein structures by using pairwise sequence-alignment benchmarks.Accurate single-sequence prediction of solvent accessible surface area using local and global featuresPredicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.SP5: improving protein fold recognition by using torsion angle profiles and profile-based gap penalty modelRefining near-native protein-protein docking decoys by local resampling and energy minimizationImproving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.Effective protein conformational sampling based on predicted torsion angles.Fluctuations of backbone torsion angles obtained from NMR-determined structures and their prediction.Fast and Accurate Accessible Surface Area Prediction Without a Sequence Profile.LRFragLib: an effective algorithm to identify fragments for de novo protein structure prediction.Sixty-five years of the long march in protein secondary structure prediction: the final stretch?Protein side chain modeling with orientation-dependent atomic force fields derived by series expansionsCharacterizing the existing and potential structural space of proteins by large-scale multiple loop permutations.regSNPs-splicing: a tool for prioritizing synonymous single-nucleotide substitution.Trends in template/fragment-free protein structure predictionCapturing Non-Local Interactions by Long Short Term Memory Bidirectional Recurrent Neural Networks for Improving Prediction of Protein Secondary Structure, Backbone Angles, Contact Numbers, and Solvent Accessibility.Community-wide assessment of protein-interface modeling suggests improvements to design methodology.DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indelsAccurate and efficient loop selections by the DFIRE-based all-atom statistical potential.Impact of human pathogenic micro-insertions and micro-deletions on post-transcriptional regulation.In-silico prediction of disorder content using hybrid sequence representation.Structure-based prediction of DNA-binding proteins by structural alignment and a volume-fraction corrected DFIRE-based energy functionProtein folding pathways and kinetics: molecular dynamics simulations of beta-strand motifs.Assembly and kinetic folding pathways of a tetrameric beta-sheet complex: molecular dynamics simulations on simplified off-lattice protein modelsThe dependence of all-atom statistical potentials on structural training database.Achieving 80% ten-fold cross-validated accuracy for secondary structure prediction by large-scale training.Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets.Protein binding site prediction using an empirical scoring functionPredicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.Small open reading frames: current prediction techniques and future prospect.
P50
Q24620741-73704947-4C50-46AA-B12F-5950F7C3B8A0Q24632911-249B9856-9BF6-4A90-A686-C781FB907E1AQ24657210-F2DE39C1-D2E2-4FA7-ADEB-08F31040D893Q28242753-A9F607BC-30D7-4CA0-9B45-4C5C1016C34DQ28268378-6B1463EF-4DA7-4EAD-9BE7-F072AEDE94D4Q28269843-6DBF0F44-0485-4372-8FA9-BC1889217158Q28727926-8DA2B350-B569-4264-8BFC-11E9A8773868Q30160191-EC2F96D3-4F53-4E94-ABB5-57B018A47E50Q30356679-BBF968EF-552D-4A1D-8BAB-0F6000AB47EDQ30363288-CF75684D-BDD5-4D95-A162-600953DF943EQ30365141-70887AF7-F4AE-465D-A68E-F1AAA70902DBQ30366585-23C63C74-8036-4C30-ADD5-5663A19D7E00Q30366705-D4E39264-8E7D-490F-9E04-F617B97CC7E3Q30369735-24692D06-3E62-4455-9F94-E5E28E3EEDD8Q30374452-FF827CD6-E557-4EDB-9CA1-29A98EED5F6CQ30375959-DFC0DF87-ABCB-4330-8661-8B55E0777C4FQ30382789-D8B561C8-B313-4759-AAF3-D9380E8BAB99Q30393337-DF1DBDBE-F0FB-4A35-AA69-8C2BC964D6D2Q30394577-48794E4A-7AEA-44B3-B374-73790D5F741EQ30394694-921BC1E4-DB49-4B7B-84CA-928F529FC6DBQ30396966-41932766-5A43-4170-969B-6F2B8E048806Q30400418-12EED3BC-3717-44DD-AB8A-5CE1ABC6D360Q30400436-B24CCE1A-D693-43CB-80B5-461480F795A2Q30400919-C435BAE5-23A4-4D27-97CA-2D324ADFF9E0Q30400985-1397F371-14DF-45A9-B59E-C361671C39C5Q30401256-7D6B7C20-5478-4781-9EC1-41762EC7D2BEQ30408207-833B10A6-93A7-4B9A-AAE6-2FEFA4FF033BQ30428334-0C294E1F-CE43-4F59-B4AF-CD0172F98FBDQ31037390-EA72E58F-C052-4D03-85AA-0D420CFE55EEQ33582790-439DFDB3-44FD-4584-A8C2-30C81F87327DQ33935454-AC830CDF-27D9-43B0-8141-1E6E7F138223Q34001405-7BCF97F6-C5B3-4574-AD9B-7564FBBFEC64Q34178469-851DC4F4-48D1-453A-8258-E84F8320200CQ34184303-4ACA3CF7-2508-463E-9932-AF7791D2BC51Q34185888-704EFAAF-A7E6-4542-AD9B-CCA82E3658F5Q34592447-848E2B65-1DD9-4B33-A9A4-8D8F338697A7Q34865111-64C6FE6C-0F6F-4882-99E8-F9D145D1FEFAQ34977466-7BBB5C7D-2312-4BC5-B619-CD6A58F2040BQ35162327-4E665E0E-BC80-4E6D-914D-13478DAB1006Q35465100-BAE447B7-1644-4FF4-86DD-51ED679F63C1
P50
description
researcher
@en
wetenschapper
@nl
հետազոտող
@hy
name
Yaoqi Zhou
@ast
Yaoqi Zhou
@en
Yaoqi Zhou
@es
Yaoqi Zhou
@nl
Yaoqi Zhou
@sl
type
label
Yaoqi Zhou
@ast
Yaoqi Zhou
@en
Yaoqi Zhou
@es
Yaoqi Zhou
@nl
Yaoqi Zhou
@sl
prefLabel
Yaoqi Zhou
@ast
Yaoqi Zhou
@en
Yaoqi Zhou
@es
Yaoqi Zhou
@nl
Yaoqi Zhou
@sl
P1053
B-3284-2009
P106
P1153
7405366766
P214
384148207612000340329
P2456
P2798
P31
P3829
P496
0000-0002-9958-5699
P569
2000-01-01T00:00:00Z
P7859
viaf-384148207612000340329