| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/gpb/HansonPLZ19 |
| P356 | DOI | 10.1016/J.GPB.2019.01.004 |
| P698 | PubMed publication ID | 32173600 |
| P2093 | author name string | Jack Hanson | |
| Yaoqi Zhou | |||
| Kuldip K Paliwal | |||
| Thomas Litfin | |||
| P2860 | cites work | MetaDisorder: a meta-server for the prediction of intrinsic disorder in proteins | Q21284325 |
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| Accurate Single-Sequence Prediction of Protein Intrinsic Disorder by an Ensemble of Deep Recurrent and Convolutional Architectures | Q58610765 | ||
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| Taxonomic Landscape of the Dark Proteomes: Whole-Proteome Scale Interplay Between Structural Darkness, Intrinsic Disorder, and Crystallization Propensity | Q59352983 | ||
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| Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life | Q84387398 | ||
| Functions of short lifetime biological structures at large: the case of intrinsically disordered proteins | Q89456605 | ||
| A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction | Q91111297 | ||
| NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning | Q91746091 | ||
| SPINE-D: accurate prediction of short and long disordered regions by a single neural-network based method | Q24620741 | ||
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| Structure of Actin-related protein 8 and its contribution to nucleosome binding | Q27673086 | ||
| p53 Proteoforms and Intrinsic Disorder: An Illustration of the Protein Structure-Function Continuum Concept | Q28079390 | ||
| HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment | Q28256122 | ||
| Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins | Q28269843 | ||
| The meaning and use of the area under a receiver operating characteristic (ROC) curve | Q29547182 | ||
| Protein disorder prediction: implications for structural proteomics | Q29615736 | ||
| GlobPlot: Exploring protein sequences for globularity and disorder | Q29615737 | ||
| Intrinsically unstructured proteins: re-assessing the protein structure-function paradigm | Q29615865 | ||
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| Sequence complexity of disordered protein | Q29616420 | ||
| Length-dependent prediction of protein intrinsic disorder. | Q30353929 | ||
| Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. | Q30375959 | ||
| Capturing 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 | Q30401256 | ||
| Intrinsic disorder here, there, and everywhere, and nowhere to escape from it. | Q30402803 | ||
| DISOPRED3: precise disordered region predictions with annotated protein-binding activity | Q30633989 | ||
| MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins | Q34280717 | ||
| DisProt: a database of protein disorder | Q34341031 | ||
| RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins | Q34425242 | ||
| MobiDB 2.0: an improved database of intrinsically disordered and mobile proteins | Q34445775 | ||
| The s2D method: simultaneous sequence-based prediction of the statistical populations of ordered and disordered regions in proteins | Q34455278 | ||
| MoRFchibi SYSTEM: software tools for the identification of MoRFs in protein sequences | Q34526381 | ||
| DisProt 7.0: a major update of the database of disordered proteins. | Q34546157 | ||
| High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder | Q36160593 | ||
| Showing your ID: intrinsic disorder as an ID for recognition, regulation and cell signaling | Q36226283 | ||
| Assessing protein disorder and induced folding | Q36312787 | ||
| Natural protein sequences are more intrinsically disordered than random sequences | Q37075588 | ||
| Intrinsically Semi-disordered State and Its Role in Induced Folding and Protein Aggregation | Q37337624 | ||
| NMR contributions to structural dynamics studies of intrinsically disordered proteins | Q38198240 | ||
| Uniclust databases of clustered and deeply annotated protein sequences and alignments. | Q38439750 | ||
| Molecular recognition features (MoRFs) in three domains of life | Q38453820 | ||
| P921 | main subject | deep learning | Q197536 |
| P577 | publication date | 2020-03-13 | |
| P1433 | published in | Genomics Proteomics and Bioinformatics | Q15814350 |
| P1476 | title | SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning |
| Q107865030 | Accurate prediction of protein structures and interactions using a three-track neural network |
| Q107868893 | Highly accurate protein structure prediction for the human proteome |
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