scholarly article | Q13442814 |
P8978 | DBLP publication ID | journals/ploscb/LiZWLGZW18 |
P356 | DOI | 10.1371/JOURNAL.PCBI.1006514 |
P932 | PMC publication ID | 6258470 |
P698 | PubMed publication ID | 30481171 |
P50 | author | Jian Zhang | Q59678165 |
P2093 | author name string | Wei Wang | |
Wei Zhu | |||
Jun Wang | |||
Jun Li | |||
Wenfei Li | |||
Sheng Gong | |||
P2860 | cites work | A probabilistic model of RNA conformational space | Q21145357 |
The rise of regulatory RNA | Q22121998 | ||
Turning limited experimental information into 3D models of RNA | Q24630208 | ||
Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters | Q24647503 | ||
iFoldRNA: three-dimensional RNA structure prediction and folding | Q24658133 | ||
Automated de novo prediction of native-like RNA tertiary structures | Q24673712 | ||
The noncoding RNA revolution-trashing old rules to forge new ones | Q26851952 | ||
Mastering the game of Go with deep neural networks and tree search | Q28005460 | ||
Long non-coding RNAs: insights into functions | Q28131764 | ||
The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data | Q28271703 | ||
An improved protein decoy set for testing energy functions for protein structure prediction. | Q30333352 | ||
Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation | Q30402159 | ||
3D deep convolutional neural networks for amino acid environment similarity analysis | Q30403066 | ||
Sequence to Structure (S2S): display, manipulate and interconnect RNA data from sequence to structure | Q30990184 | ||
A coarse-grained model with implicit salt for RNAs: predicting 3D structure, stability and salt effect. | Q51038351 | ||
RNA fragment modeling with a nucleobase discrete-state model | Q83776001 | ||
HiRE-RNA: A High Resolution Coarse-Grained Energy Model for RNA | Q84959270 | ||
A nucleotide-level coarse-grained model of RNA | Q88127110 | ||
Development and evaluation of a deep learning model for protein-ligand binding affinity prediction | Q88661460 | ||
MANIP: an interactive tool for modelling RNA. | Q31412438 | ||
RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme | Q33364669 | ||
Assemble: an interactive graphical tool to analyze and build RNA architectures at the 2D and 3D levels | Q33609575 | ||
Atomic accuracy in predicting and designing noncanonical RNA structure | Q33789129 | ||
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning | Q34487019 | ||
RNA2D3D: a program for generating, viewing, and comparing 3-dimensional models of RNA | Q34768823 | ||
ModeRNA: a tool for comparative modeling of RNA 3D structure | Q35017843 | ||
Vfold: a web server for RNA structure and folding thermodynamics prediction | Q35249784 | ||
RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures | Q35621422 | ||
An enumerative stepwise ansatz enables atomic-accuracy RNA loop modeling | Q35651055 | ||
3dRNAscore: a distance and torsion angle dependent evaluation function of 3D RNA structures | Q35656687 | ||
RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction | Q35853409 | ||
Automated and fast building of three-dimensional RNA structures | Q36316650 | ||
Predicting 3D Structure, Flexibility, and Stability of RNA Hairpins in Monovalent and Divalent Ion Solutions | Q36429548 | ||
Predicting effects of noncoding variants with deep learning-based sequence model | Q36621822 | ||
RNA in unexpected places: long non-coding RNA functions in diverse cellular contexts | Q36855920 | ||
Prediction of geometrically feasible three-dimensional structures of pseudoknotted RNA through free energy estimation | Q37426607 | ||
ProQ3D: improved model quality assessments using deep learning | Q38374451 | ||
DeepSite: Protein binding site predictor using 3D-convolutional neural networks. | Q38748498 | ||
Protein-Ligand Scoring with Convolutional Neural Networks. | Q38860867 | ||
Optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide-nucleotide interactions from direct coupling analysis. | Q40950675 | ||
Coarse-grained prediction of RNA loop structures. | Q41329375 | ||
Mastering the game of Go without human knowledge | Q42209359 | ||
RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease | Q42558500 | ||
Physics-based de novo prediction of RNA 3D structures. | Q42712420 | ||
All-atom knowledge-based potential for RNA structure prediction and assessment | Q45783105 | ||
Solving the quantum many-body problem with artificial neural networks. | Q45948562 | ||
Hierarchical Assembly of RNA Three-Dimensional Structures Based on Loop Templates. | Q47234232 | ||
Predicting RNA Structure with Vfold. | Q49289634 | ||
Non-coding RNA networks in cancer. | Q49689803 | ||
P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
P6216 | copyright status | copyrighted | Q50423863 |
P433 | issue | 11 | |
P407 | language of work or name | English | Q1860 |
P921 | main subject | molecular biology | Q7202 |
convolutional neural network | Q17084460 | ||
P304 | page(s) | e1006514 | |
P577 | publication date | 2018-11-01 | |
P1433 | published in | PLOS Computational Biology | Q2635829 |
P1476 | title | RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks | |
P478 | volume | 14 |
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