Accurate prediction of solvent accessibility using neural networks-based regression.
about
Impact of residue accessible surface area on the prediction of protein secondary structuresA composite score for predicting errors in protein structure modelsPSSM-based prediction of DNA binding sites in proteinsIdentification of a new family of putative PD-(D/E)XK nucleases with unusual phylogenomic distribution and a new type of the active siteA homology model of restriction endonuclease SfiI in complex with DNAPrediction of solvent accessibility and sites of deleterious mutations from protein sequence.The PD-(D/E)XK superfamily revisited: identification of new members among proteins involved in DNA metabolism and functional predictions for domains of (hitherto) unknown function.Molecular phylogenetics and comparative modeling of HEN1, a methyltransferase involved in plant microRNA biogenesisMembrane insertion of a Tc toxin in near-atomic detailNear-atomic resolution visualization of human transcription promoter opening.Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteinsA central domain of cyclin D1 mediates nuclear receptor corepressor activityCardiolipin Interactions with ProteinsComprehensive identification and modified-site mapping of S-nitrosylated targets in prostate epithelial cellsDiversification and coevolution of the ghrelin/growth hormone secretagogue receptor system in vertebratesPositive selection at the protein network periphery: Evaluation in terms of structural constraints and cellular contextPredicting physiologically relevant SH3 domain mediated protein-protein interactions in yeast.Network evolution: rewiring and signatures of conservation in signaling.MOTIPS: automated motif analysis for predicting targets of modular protein domainsPrediction of the burial status of transmembrane residues of helical membrane proteins.Accurate single-sequence prediction of solvent accessible surface area using local and global featuresDetecting clusters of mutationsImproving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model.Scatter-search with support vector machine for prediction of relative solvent accessibility.Prediction of protein solvent accessibility using PSO-SVR with multiple sequence-derived features and weighted sliding window schemeFast and Accurate Accessible Surface Area Prediction Without a Sequence Profile.Sixty-five years of the long march in protein secondary structure prediction: the final stretch?Accurate prediction of protein relative solvent accessibility using a balanced model.Fast geometric consensus approach for protein model quality assessmentImproving prediction of burial state of residues by exploiting correlation among residuesCapturing 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.Structural determinants of limited proteolysis.An amino acid packing code for α-helical structure and protein designThe MULTICOM toolbox for protein structure predictionThe rust transferred proteins-a new family of effector proteins exhibiting protease inhibitor function.A multi-factor model for caspase degradome predictionAccessible surface area from NMR chemical shifts.Phylogenomic analysis of the GIY-YIG nuclease superfamily.Molecular modeling and characterization of Vibrio cholerae transcription regulator HlyU
P2860
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P2860
Accurate prediction of solvent accessibility using neural networks-based regression.
description
2004 nî lūn-bûn
@nan
2004 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2004 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
name
Accurate prediction of solvent accessibility using neural networks-based regression.
@ast
Accurate prediction of solvent accessibility using neural networks-based regression.
@en
type
label
Accurate prediction of solvent accessibility using neural networks-based regression.
@ast
Accurate prediction of solvent accessibility using neural networks-based regression.
@en
prefLabel
Accurate prediction of solvent accessibility using neural networks-based regression.
@ast
Accurate prediction of solvent accessibility using neural networks-based regression.
@en
P356
P1433
P1476
Accurate prediction of solvent accessibility using neural networks-based regression
@en
P2093
Rafał Adamczak
P304
P356
10.1002/PROT.20176
P407
P577
2004-09-01T00:00:00Z