| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/bmcbi/EickholtC13 |
| P6179 | Dimensions Publication ID | 1002302872 |
| P356 | DOI | 10.1186/1471-2105-14-88 |
| P932 | PMC publication ID | 3599628 |
| P698 | PubMed publication ID | 23497251 |
| P5875 | ResearchGate publication ID | 236051685 |
| P50 | author | Jesse Eickholt | Q78293306 |
| P2093 | author name string | Jianlin Cheng | |
| P2860 | cites work | FoldUnfold: web server for the prediction of disordered regions in protein chain. | Q51932072 |
| The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. | Q52974739 | ||
| Assessment of disorder predictions in CASP8 | Q57998345 | ||
| MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins | Q21284325 | ||
| Prediction and Functional Analysis of Native Disorder in Proteins from the Three Kingdoms of Life | Q22061741 | ||
| Gapped BLAST and PSI-BLAST: a new generation of protein database search programs | Q24545170 | ||
| Target domain definition and classification in CASP8 | Q24647352 | ||
| SCRATCH: a protein structure and structural feature prediction server | Q24812422 | ||
| EMBOSS: the European Molecular Biology Open Software Suite | Q27860491 | ||
| Evaluation of disorder predictions in CASP9 | Q29048164 | ||
| The meaning and use of the area under a receiver operating characteristic (ROC) curve | Q29547182 | ||
| Intrinsically unstructured proteins | Q29614784 | ||
| Why are "natively unfolded" proteins unstructured under physiologic conditions? | Q29615739 | ||
| IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content | Q29615888 | ||
| Intrinsic disorder and protein function | Q29616415 | ||
| Natively unfolded proteins: a point where biology waits for physics | Q29616416 | ||
| Exploiting heterogeneous sequence properties improves prediction of protein disorder. | Q30351476 | ||
| Protein disorder prediction at multiple levels of sensitivity and specificity | Q30368470 | ||
| PreDisorder: ab initio sequence-based prediction of protein disordered regions. | Q30383714 | ||
| A comprehensive overview of computational protein disorder prediction methods | Q30406461 | ||
| CASP9 target classification | Q30408141 | ||
| Reducing the dimensionality of data with neural networks | Q31050179 | ||
| Solving the protein sequence metric problem | Q33772010 | ||
| A fast learning algorithm for deep belief nets | Q33996665 | ||
| Training products of experts by minimizing contrastive divergence | Q34144628 | ||
| Natively unstructured regions in proteins identified from contact predictions | Q34665088 | ||
| Predicting protein residue-residue contacts using deep networks and boosting | Q36432211 | ||
| To recognize shapes, first learn to generate images | Q36966396 | ||
| Predicting intrinsic disorder in proteins: an overview | Q37549950 | ||
| Proteins without 3D structure: definition, detection and beyond | Q37865281 | ||
| Discovering binary codes for documents by learning deep generative models | Q38496334 | ||
| CSpritz: accurate prediction of protein disorder segments with annotation for homology, secondary structure and linear motifs | Q41827676 | ||
| PrDOS: prediction of disordered protein regions from amino acid sequence | Q42431942 | ||
| Drugs for ‘protein clouds’: targeting intrinsically disordered transcription factors | Q43445939 | ||
| ESpritz: accurate and fast prediction of protein disorder | Q44385437 | ||
| Rational drug design via intrinsically disordered protein | Q47193604 | ||
| P275 | copyright license | Creative Commons Attribution 2.0 Generic | Q19125117 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P921 | main subject | deep learning | Q197536 |
| P304 | page(s) | 88 | |
| P577 | publication date | 2013-03-06 | |
| P1433 | published in | BMC Bioinformatics | Q4835939 |
| P1476 | title | DNdisorder: predicting protein disorder using boosting and deep networks | |
| P478 | volume | 14 |
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| Q30377015 | An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions. |
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| Q39356263 | Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions |
| Q30633989 | DISOPRED3: precise disordered region predictions with annotated protein-binding activity |
| Q50420138 | Deep Learning and its Applications in Biomedicine. |
| Q26740441 | Deep learning for computational biology |
| Q41704702 | Deep learning methods for protein torsion angle prediction |
| Q43241621 | DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields |
| Q30375859 | DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach. |
| Q30392517 | Estimation of Position Specific Energy as a Feature of Protein Residues from Sequence Alone for Structural Classification |
| Q38944980 | How disordered is my protein and what is its disorder for? A guide through the "dark side" of the protein universe |
| Q40244086 | Improving Protein Fold Recognition by Deep Learning Networks |
| Q30375959 | Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. |
| Q30357086 | On the encoding of proteins for disordered regions prediction. |
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